WO2019032306A9 - Prédiction d'événements d'inventaire à l'aide d'une différenciation sémantique - Google Patents

Prédiction d'événements d'inventaire à l'aide d'une différenciation sémantique Download PDF

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Publication number
WO2019032306A9
WO2019032306A9 PCT/US2018/043937 US2018043937W WO2019032306A9 WO 2019032306 A9 WO2019032306 A9 WO 2019032306A9 US 2018043937 W US2018043937 W US 2018043937W WO 2019032306 A9 WO2019032306 A9 WO 2019032306A9
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WIPO (PCT)
Prior art keywords
images
subjects
identified
cameras
sequences
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PCT/US2018/043937
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English (en)
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WO2019032306A1 (fr
Inventor
Jordan E. Fisher
Daniel L. Fischetti
Brandon L. Ogle
John F. Novak
Kyle E. Dorman
Kenneth S. Kihara
Juan C. Lasheras
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Standard Cognition, Corp.
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
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Publication date
Priority claimed from US15/847,796 external-priority patent/US10055853B1/en
Priority claimed from US15/907,112 external-priority patent/US10133933B1/en
Priority claimed from US15/945,473 external-priority patent/US10474988B2/en
Priority claimed from US15/945,466 external-priority patent/US10127438B1/en
Application filed by Standard Cognition, Corp. filed Critical Standard Cognition, Corp.
Priority to EP18843163.9A priority Critical patent/EP3665615A4/fr
Priority to JP2020507521A priority patent/JP7191088B2/ja
Priority to CA3072058A priority patent/CA3072058A1/fr
Publication of WO2019032306A1 publication Critical patent/WO2019032306A1/fr
Publication of WO2019032306A9 publication Critical patent/WO2019032306A9/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q20/00—Payment architectures, schemes or protocols
    • G06Q20/08—Payment architectures
    • G06Q20/20—Point-of-sale [POS] network systems
    • G06Q20/203—Inventory monitoring
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
    • G06F18/2413—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on distances to training or reference patterns
    • G06F18/24133—Distances to prototypes
    • G06F18/24143—Distances to neighbourhood prototypes, e.g. restricted Coulomb energy networks [RCEN]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/045—Combinations of networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G06N3/084—Backpropagation, e.g. using gradient descent
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
    • G06Q10/087—Inventory or stock management, e.g. order filling, procurement or balancing against orders
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q20/00—Payment architectures, schemes or protocols
    • G06Q20/08—Payment architectures
    • G06Q20/20—Point-of-sale [POS] network systems
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q20/00—Payment architectures, schemes or protocols
    • G06Q20/08—Payment architectures
    • G06Q20/20—Point-of-sale [POS] network systems
    • G06Q20/208—Input by product or record sensing, e.g. weighing or scanner processing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/06—Buying, selling or leasing transactions
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/40—Extraction of image or video features
    • G06V10/44—Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components
    • G06V10/443—Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components by matching or filtering
    • G06V10/449—Biologically inspired filters, e.g. difference of Gaussians [DoG] or Gabor filters
    • G06V10/451—Biologically inspired filters, e.g. difference of Gaussians [DoG] or Gabor filters with interaction between the filter responses, e.g. cortical complex cells
    • G06V10/454—Integrating the filters into a hierarchical structure, e.g. convolutional neural networks [CNN]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/82—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/50—Context or environment of the image
    • G06V20/52—Surveillance or monitoring of activities, e.g. for recognising suspicious objects
    • G—PHYSICS
    • G07—CHECKING-DEVICES
    • G07G—REGISTERING THE RECEIPT OF CASH, VALUABLES, OR TOKENS
    • G07G1/00—Cash registers
    • G07G1/0036—Checkout procedures
    • G07G1/0045—Checkout procedures with a code reader for reading of an identifying code of the article to be registered, e.g. barcode reader or radio-frequency identity [RFID] reader
    • G07G1/0054—Checkout procedures with a code reader for reading of an identifying code of the article to be registered, e.g. barcode reader or radio-frequency identity [RFID] reader with control of supplementary check-parameters, e.g. weight or number of articles
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N17/00—Diagnosis, testing or measuring for television systems or their details
    • H04N17/002—Diagnosis, testing or measuring for television systems or their details for television cameras
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N23/00—Cameras or camera modules comprising electronic image sensors; Control thereof
    • H04N23/90—Arrangement of cameras or camera modules, e.g. multiple cameras in TV studios or sports stadiums

Definitions

  • a difficult problem in image processing arises when images from multiple cameras disposed over large spaces are used to identify and track actions of subjects.
  • a system and method are provided for tracking put and takes of inventory items by subjects in an area of real space including inventory display structures that comprise using a plurality of cameras disposed above the inventory display structures to produce respective sequences of images of inventory display structures in corresponding fields of view in the real space, the field of view of each camera overlapping with the field of view of at least one other camera in the plurality of cameras.
  • a system and method are described for detecting puts and takes of inventory items by identifying semantically significant changes in the sequences of images relating to inventory items on inventory display structures and associating the semantically significant changes with subjects represented in the sequences of images.
  • the system uses a plurality of cameras to produce respective sequences of images of corresponding fields of view in the real space.
  • the field of view of each camera overlaps with the field of view of at least one other camera in the plurality of cameras.
  • the system includes first image processors, including subject image recognition engines, receiving corresponding sequences of images from the plurality of cameras.
  • the first images processors process images to identify subjects represented in the images in the corresponding sequences of images.
  • the system further includes, second image processors, including background image recognition engines, receiving corresponding sequences of images from the plurality of cameras.
  • the second image processors mask the identified subjects to generate masked images, and process the masked images to identify and classify background changes represented in the images in the corresponding sequences of images.
  • the mask logic combines sets of N masked images in the sequences of images to generate sequences of factored images for each camera.
  • the second image processors identify and classify background changes by processing the sequence of factored images.
  • the second image processors include logic to produce change data structures for the corresponding sequences of images.
  • the change data structures include coordinates in the masked images of identified background changes, identifiers of an inventory item subject of the identified background changes and classifications of the identified background changes.
  • the second image processors further include coordination logic to process change data structures from sets of cameras having overlapping fields of view to locate the identified background changes in real space.
  • the classifications of identified background changes in the change data structures indicate whether the identified inventory item has been added or removed relative to the background image.
  • the classifications of identified background changes in the change data structures indicate whether the identified inventory item has been added or removed relative to the background image.
  • the system further includes logic to associate background changes with identified subjects.
  • the system includes the logic to make detections of takes of inventory items by the identified subjects and of puts of inventory items on inventory display structures by the identified subjects.
  • the system includes logic to associate background changes with identified subjects.
  • the system further includes the logic to make detections of takes of inventory items by the identified subjects and of puts of inventory items on inventory display structures by the identified subjects.
  • the system can include third image processors as described herein, including foreground image recognition engines, receiving corresponding sequences of images from the plurality of cameras.
  • the third image processors process images to identify and classify foreground changes represented in the images in the corresponding sequences of images.
  • the system identifies constellations of candidate joints, where the constellations include respective sets of candidate joints having coordinates in real space, as multi -joint subjects in the real space.
  • the image recognition engines comprise convolutional neural networks.
  • the processing of images by image recognition engines includes generating confidence arrays for elements of the image.
  • a confidence array for a particular element of an image includes confidence values for a plurality of joint types for the particular element.
  • the confidence arrays are used to select a joint type for the joint data structure of the particular element based on the confidence array.
  • identifying sets of candidate joints comprises applying heuristic functions based on physical relationships among joints of subjects in real space to identify sets of candidate joints as multi -joint subjects.
  • the processing includes storing the sets of joints identified as multi -joint subjects. Identifying sets of candidate joints includes determining whether a candidate joint identified in images taken at a particular time corresponds with a member of one of the sets of candidate joints identified as multi -joint subjects in a preceding image.
  • the sequences of images are synchronized so that images in each of the sequences of images captured by the plurality of cameras represent the real space at a single point in time on the time scale of the movement of subjects through the space.
  • the coordinates in real space of members of a set of candidate joints identified as a multi -joint subject identify locations in the area of the multi -joint subject.
  • the system processes images in the sequences of images received from the plurality of cameras to identify subjects represented in the images and generate classifications of the identified subjects. Finally, the system processes the classifications of identified subjects for sets of images in the sequences of images to detect takes of inventory items by identified subjects and puts of inventory items on shelves by identified subjects.
  • the classification identifies whether the identified subject is holding an inventory item.
  • the classification also identifies whether a hand of the identified subject is near a shelf or whether a hand of the identified subject is near the identified subject.
  • the classification of whether the hand is near the identified subject can include whether a hand of the identified subject is near to a basket associated with an identified subject, and near to the body of the identified subject.
  • the system can make a first set of detections of takes of inventory items by the identified subjects and of puts of inventory items on inventory display structures by the identified subjects, and a second set of detections of takes of inventory items by the identified subjects and of puts of inventory items on inventory display structures by the identified subjects.
  • Selection logic to process the first and second sets of detections can be used to generate log data structures.
  • the log data structures include lists of inventory items for identified subjects.
  • the sequences of images from cameras in the plurality of cameras are synchronized.
  • the same cameras and the same sequences of images are used by both the foreground and background image processors in one preferred implementation.
  • redundant detections of puts and takes of inventory items are made using the same input data allowing for high confidence, and high accuracy, in the resulting data.
  • the system comprises logic to detect puts and takes of inventory items by identifying gestures of subjects and inventory items associated with the gestures represented in the sequences of images. This can be done using foreground image recognition engines in coordination with subject image recognition engines as described herein.
  • the system comprises logic to detect puts and takes of inventory items by identifying semantically significant changes in inventory items on inventory display structures, such as shelves, over time and associating the semantically significant changes with subjects represented in the sequences of images. This can be done using background image recognition engines in coordination with subject image recognition engines as described herein.
  • gesture analysis and semantic difference analysis can be combined, and executed on the same sequences of synchronized images from an array of cameras.
  • FIG. 1 illustrates an architectural level schematic of a system in which a tracking engine tracks subjects using joint data generated by image recognition engines.
  • FIG. 2 is a side view of an aisle in a shopping store illustrating a camera arrangement.
  • FIG. 3 is a top view of the aisle of Fig. 2 in a shopping store illustrating a camera arrangement.
  • Fig. 4 is a camera and computer hardware arrangement configured for hosting an image recognition engine of Fig. 1.
  • Fig. 5 illustrates a convolutional neural network illustrating identification of joints in an image recognition engine of Fig. 1.
  • Fig. 6 shows an example data structure for storing joint information.
  • Fig. 7 illustrates the tracking engine of Fig. 1 with a global metric calculator.
  • Fig. 8 shows an example data structure for storing a subject including the information of associated joints.
  • Fig. 9 is a flowchart illustrating process steps for tracking subjects by the system of Fig. 1.
  • Fig. 10 is a flowchart showing more detailed process steps for a camera calibration step of Fig. 9.
  • Fig. 11 is a flowchart showing more detailed process steps for a video process step of Fig. 9.
  • Fig. 12B is a flowchart showing a second part of more detailed process steps for the scene process of Fig. 9.
  • FIG. 13 is an illustration of an environment in which an embodiment of the system of Fig. 1 is used.
  • Fig. 14 is an illustration of video and scene processes in an embodiment of the system of Fig. 1.
  • Fig. l5a is a schematic showing a pipeline with multiple convolutional neural networks (CNNs) including joints-CNN, WhatCNN and WhenCNN to generate a shopping cart data structure per subject in the real space.
  • CNNs convolutional neural networks
  • Fig. l5b shows multiple image channels from multiple cameras and coordination logic for the subjects and their respective shopping cart data structures.
  • Fig. 16 is a flowchart illustrating process steps for identifying and updating subjects in the real space.
  • Fig. 17 is a flowchart showing process steps for processing hand joints of subjects to identify inventory items.
  • Fig. 18 is a flowchart showing process steps for time series analysis of inventory items per hand joint to create a shopping cart data structure per subject.
  • FIG. 19 is an illustration of a WhatCNN model in an embodiment of the system of
  • FIG. 20 is an illustration of a WhenCNN model in an embodiment of the system of Fig. l5a.
  • Fig. 21 presents an example architecture of a WhatCNN model identifying the dimensionality of convolutional layers.
  • Fig. 22 presents a high level block diagram of an embodiment of a WhatCNN model for classification of hand images.
  • FIG. 23 presents details of a first block of the high level block diagram of a
  • Fig. 24 presents operators in a fully connected layer in the example WhatCNN model presented in Fig. 22.
  • Fig. 25 is an example name of an image file stored as part of the training data set for a WhatCNN model.
  • Fig. 26 is a high level architecture of a system for tracking changes by subjects in an area of real space in which a selection logic selects between a first detection using background semantic diffing and a redundant detection using foreground region proposals.
  • Fig. 27 presents components of subsystems implementing the system of Fig. 26.
  • Fig. 28A is a flowchart showing a first part of detailed process steps for determining inventory events and generation of the shopping cart data structure.
  • Fig. 28B is a flowchart showing a second part of detailed process steps for determining inventory events and generation of the shopping cart data structure.
  • FIG. 1 An architectural level schematic of a system in accordance with an implementation. Because Fig.
  • FIG. 1 The discussion of Fig. 1 is organized as follows. First, the elements of the system are described, followed by their interconnections. Then, the use of the elements in the system is described in greater detail.
  • Fig. 1 provides a block diagram level illustration of a system 100.
  • the system 100 includes cameras 114, network nodes hosting image recognition engines 1 l2a, 1 l2b, and 112h, a tracking engine 110 deployed in a network node (or nodes) on the network, a calibrator 120, a subject database 140, a training database 150, a heuristics database 160 for joints heuristics, for put and take heuristics, and other heuristics for coordinating and combining the outputs of multiple image recognition engines as described below, a calibration database 170, and a communication network or networks 181.
  • the network nodes can host only one image recognition engine, or several image recognition engines as described herein.
  • the system can also include an inventory database and other supporting data.
  • a network node is an addressable hardware device or virtual device that is attached to a network, and is capable of sending, receiving, or forwarding information over a communications channel to or from other network nodes.
  • Examples of electronic devices which can be deployed as hardware network nodes include all varieties of computers, workstations, laptop computers, handheld computers, and smartphones.
  • Network nodes can be implemented in a cloud-based server system. More than one virtual device configured as a network node can be implemented using a single physical device.
  • network nodes hosting image recognition engines are shown in the system 100. However, any number of network nodes hosting image recognition engines can be connected to the tracking engine 110 through the network(s) 181. Also, an image recognition engine, a tracking engine and other processing engines described herein can execute using more than one network node in a distributed architecture.
  • Network(s) 181 couples the network nodes lOla, lOlb, and lOlc, respectively, hosting image recognition engines 1 l2a, 1 l2b, and 112h, the network node 102 hosting the tracking engine 110, the calibrator 120, the subject database 140, the training database 150, the joints heuristics database 160, and the calibration database 170.
  • Cameras 114 are connected to the tracking engine 110 through network nodes hosting image recognition engines 1 l2a, 1 l2b, and 112h.
  • the cameras 114 are installed in a shopping store (such as a supermarket) such that sets of cameras 114 (two or more) with overlapping fields of view are positioned over each aisle to capture images of real space in the store.
  • a shopping store such as a supermarket
  • the cameras 114 are installed over aisles with overlapping fields of view. In such an embodiment, the cameras are configured with the goal that customers moving in the aisles of the shopping store are present in the field of view of two or more cameras at any moment in time.
  • Cameras 114 can be synchronized in time with each other, so that images are captured at the same time, or close in time, and at the same image capture rate.
  • the cameras 114 can send respective continuous streams of images at a predetermined rate to network nodes hosting image recognition engines 1 l2a-l 12h. Images captured in all the cameras covering an area of real space at the same time, or close in time, are synchronized in the sense that the synchronized images can be identified in the processing engines as representing different views of subjects having fixed positions in the real space.
  • the cameras send image frames at the rates of 30 frames per second (fps) to respective network nodes hosting image recognition engines 1 l2a-l 12h. Each frame has a timestamp, identity of the camera (abbreviated as“camera id”), and a frame identity (abbreviated as“frame id”) along with the image data.
  • Cameras installed over an aisle are connected to respective image recognition engines.
  • the two cameras installed over the aisle 1 l6a are connected to the network node lOla hosting an image recognition engine 1 l2a.
  • the two cameras installed over aisle 1 l6b are connected to the network node 10 lb hosting an image recognition engine 1 l2b.
  • Each image recognition engine 112a- 112h hosted in a network node or nodes lOla- 101h separately processes the image frames received from one camera each in the illustrated example.
  • each image recognition engine 1 l2a, 1 l2b, and 112h is implemented as a deep learning algorithm such as a convolutional neural network (abbreviated CNN).
  • the CNN is trained using a training database 150.
  • image recognition of subjects in the real space is based on identifying and grouping joints recognizable in the images, where the groups of joints can be attributed to an individual subject.
  • the training database 150 has a large collection of images for each of the different types of joints for subjects.
  • the subjects are the customers moving in the aisles between the shelves.
  • the system 100 is referred to as a“training system”.
  • the CNN After training the CNN using the training database 150, the CNN is switched to production mode to process images of customers in the shopping store in real time.
  • the system 100 is referred to as a runtime system (also referred to as an inference system).
  • the CNN in each image recognition engine produces arrays of joints data structures for images in its respective stream of images.
  • an array of joints data structures is produced for each processed image, so that each image recognition engine 112a- 112h produces an output stream of arrays of joints data structures.
  • These arrays of joints data structures from cameras having overlapping fields of view are further processed to form groups of joints, and to identify such groups of joints as subjects.
  • the cameras 114 are calibrated before switching the CNN to production mode.
  • the calibrator 120 calibrates the cameras and stores the calibration data in the calibration database 170.
  • the tracking engine 110 hosted on the network node 102, receives continuous streams of arrays of joints data structures for the subjects from image recognition engines 112a- H2n.
  • the tracking engine 110 processes the arrays of joints data structures and translates the coordinates of the elements in the arrays of joints data structures corresponding to images in different sequences into candidate joints having coordinates in the real space.
  • the combination of candidate joints identified throughout the real space can be considered, for the purposes of analogy, to be like a galaxy of candidate joints.
  • movement of the candidate joints is recorded so that the galaxy changes over time.
  • the output of the tracking engine 110 is stored in the subject database 140.
  • the tracking engine 110 uses logic to identify groups or sets of candidate joints having coordinates in real space as subjects in the real space.
  • each set of candidate points is like a constellation of candidate joints at each point in time.
  • the constellations of candidate joints can move over time.
  • the logic to identify sets of candidate joints comprises heuristic functions based on physical relationships amongst joints of subjects in real space. These heuristic functions are used to identify sets of candidate joints as subjects.
  • the heuristic functions are stored in heuristics database 160.
  • the output of the tracking engine 110 is stored in the subject database 140.
  • the sets of candidate joints comprise individual candidate joints that have relationships according to the heuristic parameters with other individual candidate joints and subsets of candidate joints in a given set that has been identified, or can be identified, as an individual subject.
  • the actual communication path through the network 181 can be point-to-point over public and/or private networks.
  • the communications can occur over a variety of networks 181, e.g., private networks, VPN, MPLS circuit, or Internet, and can use appropriate application programming interfaces (APIs) and data interchange formats, e.g., Representational State
  • the communication is generally over a network such as a LAN (local area network), WAN (wide area network), telephone network (Public Switched Telephone Network (PSTN), Session Initiation Protocol (SIP), wireless network, point-to-point network, star network, token ring network, hub network, Internet, inclusive of the mobile Internet, via protocols such as EDGE, 3G, 4G LTE, Wi-Fi, and WiMAX.
  • a variety of authorization and authentication techniques such as usemame/password, Open Authorization (OAuth), Kerberos, SecurelD, digital certificates and more, can be used to secure the communications.
  • the technology disclosed herein can be implemented in the context of any computer-implemented system including a database system, a multi-tenant environment, or a relational database implementation like an OracleTM compatible database implementation, an IBM DB2 Enterprise ServerTM compatible relational database implementation, a MySQLTM or PostgreSQLTM compatible relational database implementation or a Microsoft SQL ServerTM compatible relational database implementation or a NoSQLTM non-relational database implementation such as a VampireTM compatible non-relational database implementation, an Apache CassandraTM compatible non-relational database implementation, a BigTableTM compatible non-relational database implementation or an HBaseTM or DynamoDBTM compatible non-relational database implementation.
  • a relational database implementation like an OracleTM compatible database implementation, an IBM DB2 Enterprise ServerTM compatible relational database implementation, a MySQLTM or PostgreSQLTM compatible relational database implementation or a Microsoft SQL ServerTM compatible relational database implementation or a NoSQLTM non-relational database implementation such as a VampireTM
  • the technology disclosed can be implemented using different programming models like MapReduceTM, bulk synchronous programming, MPI primitives, etc. or different scalable batch and stream management systems like Apache StormTM, Apache SparkTM, Apache KafkaTM, Apache FlinkTM, TruvisoTM, Amazon Elasticsearch ServiceTM, Amazon Web ServicesTM (AWS), IBM Info-SphereTM, BorealisTM, and Yahoo! S4TM.
  • Apache StormTM Apache SparkTM, Apache KafkaTM, Apache FlinkTM, TruvisoTM, Amazon Elasticsearch ServiceTM, Amazon Web ServicesTM (AWS), IBM Info-SphereTM, BorealisTM, and Yahoo! S4TM.
  • the cameras 114 are arranged to track multi -joint entities (or subjects) in a three- dimensional (abbreviated as 3D) real space.
  • the real space can include the area of the shopping store where items for sale are stacked in shelves.
  • a point in the real space can be represented by an (x, y, z) coordinate system.
  • Each point in the area of real space for which the system is deployed is covered by the fields of view of two or more cameras 114.
  • the shelves and other inventory display structures can be arranged in a variety of manners, such as along the walls of the shopping store, or in rows forming aisles or a combination of the two arrangements.
  • Fig. 2 shows an arrangement of shelves, forming an aisle 116a, viewed from one end of the aisle 116a.
  • Two cameras, camera A 206 and camera B 208 are positioned over the aisle 116a at a predetermined distance from a roof 230 and a floor 220 of the shopping store above the inventory display structures such as shelves.
  • the cameras 114 comprise cameras disposed over and having fields of view encompassing respective parts of the inventory display structures and floor area in the real space.
  • the coordinates in real space of members of a set of candidate joints, identified as a subject identify locations in the floor area of the subject.
  • the real space can include all of the floor 220 in the shopping store from which inventory can be accessed.
  • Cameras 114 are placed and oriented such that areas of the floor 220 and shelves can be seen by at least two cameras.
  • the cameras 114 also cover at least part of the shelves 202 and 204 and floor space in front of the shelves 202 and 204.
  • Camera angles are selected to have both steep perspective, straight down, and angled perspectives that give more full body images of the customers.
  • the cameras 114 are configured at an eight (8) foot height or higher throughout the shopping store.
  • Fig. 13 presents an illustration of such an embodiment.
  • the cameras 206 and 208 have overlapping fields of view, covering the space between a shelf A 202 and a shelf B 204 with overlapping fields of view 216 and 218, respectively.
  • a location in the real space is represented as a (x, y, z) point of the real space coordinate system“x” and“y” represent positions on a two-dimensional (2D) plane which can be the floor 220 of the shopping store.
  • the value“z” is the height of the point above the 2D plane at floor 220 in one configuration.
  • FIG. 3 illustrates the aisle 1 l6a viewed from the top of Fig. 2, further showing an example arrangement of the positions of cameras 206 and 208 over the aisle 1 l6a.
  • the cameras 206 and 208 are positioned closer to opposite ends of the aisle 1 l6a.
  • the camera A 206 is positioned at a predetermined distance from the shelf A 202 and the camera B 208 is positioned at a predetermined distance from the shelf B 204.
  • the cameras are positioned at equal distances from each other.
  • two cameras are positioned close to the opposite ends and a third camera is positioned in the middle of the aisle. It is understood that a number of different camera arrangements are possible.
  • the camera calibrator 120 performs two types of calibrations: internal and external. In internal calibration, the internal parameters of the cameras 114 are calibrated.
  • Examples of internal camera parameters include focal length, principal point, skew, fisheye coefficients, etc.
  • a variety of techniques for internal camera calibration can be used. One such technique is presented by Zhang in“A flexible new technique for camera calibration” published in IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume 22, No. 11, November 2000.
  • the external camera parameters are calibrated in order to generate mapping parameters for translating the 2D image data into 3D coordinates in real space.
  • one subject such as a person, is introduced into the real space. The subject moves through the real space on a path that passes through the field of view of each of the cameras 114. At any given point in the real space, the subject is present in the fields of view of at least two cameras forming a 3D scene.
  • the two cameras have a different view of the same 3D scene in their respective two-dimensional (2D) image planes.
  • a feature in the 3D scene such as a left- wrist of the subject is viewed by two cameras at different positions in their respective 2D image planes.
  • a point correspondence is established between every pair of cameras with overlapping fields of view for a given scene. Since each camera has a different view of the same 3D scene, a point correspondence is two pixel locations (one location from each camera with overlapping field of view) that represent the projection of the same point in the 3D scene. Many point correspondences are identified for each 3D scene using the results of the image recognition engines 112a- 112h for the purposes of the external calibration.
  • the image recognition engines identify the position of a joint as (x, y) coordinates, such as row and column numbers, of pixels in the 2D image planes of respective cameras 114.
  • a joint is one of 19 different types of joints of the subject. As the subject moves through the fields of view of different cameras, the tracking engine 110 receives (x, y) coordinates of each of the 19 different types of joints of the subject used for the calibration from cameras 114 per image.
  • images are streamed off of all cameras at a rate of 30 FPS (frames per second) or more and a resolution of 720 pixels in full RGB (red, green, and blue) color. These images are in the form of one-dimensional arrays (also referred to as flat arrays).
  • FPS frames per second
  • RGB red, green, and blue
  • These images are in the form of one-dimensional arrays (also referred to as flat arrays).
  • the large number of images collected above for a subject can be used to determine corresponding points between cameras with overlapping fields of view. Consider two cameras A and B with overlapping field of view. The plane passing through camera centers of cameras A and B and the joint location (also referred to as feature point) in the 3D scene is called the“epipolar plane”.
  • This non-linear optimization function is used by the tracking engine 110 to identify the same joints in outputs (arrays of joints data structures) of different image recognition engines H2a-l l2n, processing images of cameras 114 with overlapping fields of view.
  • the results of the internal and external camera calibration are stored in the calibration database 170.
  • a variety of techniques for determining the relative positions of the points in images of cameras 114 in the real space can be used.
  • Longuet-Higgins published, “A computer algorithm for reconstructing a scene from two projections” in Nature, Volume 293, 10 September 1981. This paper presents computing a three-dimensional structure of a scene from a correlated pair of perspective projections when spatial relationship between the two projections is unknown.
  • the Longuet-Higgins paper presents a technique to determine the position of each camera in the real space with respect to other cameras. Additionally, their technique allows triangulation of a subject in the real space, identifying the value of the z-coordinate (height from the floor) using images from cameras 114 with overlapping fields of view.
  • An arbitrary point in the real space for example, the end of a shelf in one comer of the real space, is designated as a (0, 0, 0) point on the (x, y, z) coordinate system of the real space.
  • the parameters of the external calibration are stored in two data structures.
  • the first data structure stores intrinsic parameters.
  • the intrinsic parameters represent a projective transformation from the 3D coordinates into 2D image coordinates.
  • the first data structure contains intrinsic parameters per camera as shown below.
  • the data values are all numeric floating point numbers.
  • This data structure stores a 3x3 intrinsic matrix, represented as“K” and distortion coefficients.
  • the distortion coefficients include six radial distortion coefficients and two tangential distortion coefficients. Radial distortion occurs when light rays bend more near the edges of a lens than they do at its optical center. Tangential distortion occurs when the lens and the image plane are not parallel.
  • the following data structure shows values for the first camera only. Similar data is stored for all the cameras 114.
  • the second data structure stores per pair of cameras: a 3x3 fundamental matrix
  • F a 3x3 essential matrix
  • E a 3x4 projection matrix
  • R a 3x3 rotation matrix
  • t a 3x1 translation vector
  • This data is used to convert points in one camera’s reference frame to another camera’s reference frame.
  • eight homography coefficients are also stored to map the plane of the floor 220 from one camera to another.
  • a fundamental matrix is a relationship between two images of the same scene that constrains where the projection of points from the scene can occur in both images.
  • Essential matrix is also a relationship between two images of the same scene with the condition that the cameras are calibrated.
  • the projection matrix gives a vector space projection from 3D real space to a subspace.
  • the rotation matrix is used to perform a rotation in Euclidean space.
  • Translation vector“t” represents a geometric transformation that moves every point of a figure or a space by the same distance in a given direction.
  • the homography floor coefficients are used to combine images of features of subjects on the floor 220 viewed by cameras with overlapping fields of views.
  • the second data structure is shown below. Similar data is stored for all pairs of cameras. As indicated previously, the x’s represents numeric floating point numbers.
  • homography floor coefficients [x, x, x, x, x, x, x]
  • Fig. 4 presents an architecture 400 of a network hosting image recognition engines.
  • the system includes a plurality of network nodes lOla-lOln in the illustrated embodiment.
  • the network nodes are also referred to as processing platforms.
  • Processing platforms lOla-lOln and cameras 412, 414, 416, ... 418 are connected to network(s) 481.
  • Fig. 4 shows a plurality of cameras 412, 414, 416, ... 418 connected to the network(s).
  • the cameras 412 to 418 are connected to the network(s) 481 using Ethernet-based connectors 422, 424, 426, and 428, respectively.
  • the Ethernet-based connectors have a data transfer speed of 1 gigabit per second, also referred to as Gigabit Ethernet.
  • cameras 114 are connected to the network using other types of network connections which can have a faster or slower data transfer rate than Gigabit Ethernet.
  • a set of cameras can be connected directly to each processing platform, and the processing platforms can be coupled to a network.
  • Storage subsystem 430 stores the basic programming and data constructs that provide the functionality of certain embodiments of the present invention.
  • the various modules implementing the functionality of a plurality of image recognition engines may be stored in storage subsystem 430.
  • the storage subsystem 430 is an example of a computer readable memory comprising a non-transitory data storage medium, having computer instructions stored in the memory executable by a computer to perform the all or any combination of the data processing and image processing functions described herein, including logic to identify changes in real space, to track subjects and to detect puts and takes of inventory items in an area of real space by processes as described herein.
  • the computer instructions can be stored in other types of memory, including portable memory, that comprise a non-transitory data storage medium or media, readable by a computer.
  • a host memory subsystem 432 typically includes a number of memories including a main random access memory (RAM) 434 for storage of instructions and data during program execution and a read-only memory (ROM) 436 in which fixed instructions are stored.
  • RAM main random access memory
  • ROM read-only memory
  • the RAM 434 is used as a buffer for storing video streams from the cameras 114 connected to the platform 101 a.
  • a file storage subsystem 440 provides persistent storage for program and data files.
  • the storage subsystem 440 includes four 120 Gigabyte (GB) solid state disks (SSD) in a RAID 0 (redundant array of independent disks) arrangement identified by a numeral 442.
  • the RAID 0 442 is used to store training data.
  • the training data which is not in RAM 434 is read from RAID 0 442.
  • the hard disk drive (HDD) 446 is a 10 terabyte storage. It is slower in access speed than the RAID 0 442 storage.
  • the solid state disk (SSD) 444 contains the operating system and related files for the image recognition engine 1 l2a.
  • bus subsystem 454 is shown schematically as a single bus, alternative embodiments of the bus subsystem may use multiple busses.
  • a 2 x 2 filter 520 is convolved with the input image 510.
  • no padding is applied when the filter is convolved with the input.
  • a nonlinearity function is applied to the convolved image.
  • rectified linear unit (ReLU) activations are used.
  • Other examples of nonlinear functions include sigmoid, hyperbolic tangent (tanh) and variations of ReLU such as leaky ReLU.
  • a search is performed to find hyper parameter values.
  • the hyper-parameters are C
  • a second approach to solve this problem is to use heuristics to reduce possible combinations of joints identified as members of a set of candidate joints for a single subject. For example, a left- wrist joint cannot belong to a subject far apart in space from other joints of the subject because of known physiological characteristics of the relative positions of joints.
  • the second category of heuristics includes metrics to ascertain similarity between two proposed subject-joint locations from the fields of view of multiple cameras at the same moment in time.
  • these metrics are floating point values, where higher values mean two lists of joints are likely to belong to the same subject.
  • the second set of metrics determines the distance between a customer’s same joints in image frames from two or more cameras (with overlapping fields of view) at the same moment in time.
  • the third category of heuristics include metrics to ascertain similarity between all joints of a proposed subject-joint location in the same camera view at the same moment in time.
  • this category of metrics determines distance between joints of a customer in one frame from one camera.
  • the fourth category of heuristics includes metrics to ascertain dissimilarity between proposed subject-joint locations.
  • these metrics are floating point values. Higher values mean two lists of joints are more likely to not be the same subject.
  • two example metrics in this category include:
  • Thresholds to determine when a subject is no longer in the real space.
  • the tracking engine 110 includes logic to store the sets of joints identified as subjects.
  • the logic to identify sets of candidate joints includes logic to determine whether a candidate joint identified in images taken at a particular time corresponds with a member of one of the sets of candidate joints identified as subjects in preceding images.
  • the tracking engine 110 compares the current joint-locations of a subject with previously recorded joint-locations of the same subject at regular intervals. This comparison allows the tracking engine 110 to update the joint locations of subjects in the real space. Additionally, using this, the tracking engine 110 identifies false positives (i.e.. falsely identified subjects) and removes subjects no longer present in the real space.
  • the system identifies joints of a subject and creates a skeleton of the subject.
  • the skeleton is projected into the real space indicating the position and orientation of the subject in the real space. This is also referred to as“pose estimation” in the field of machine vision.
  • the system displays orientations and positions of subjects in the real space on a graphical user interface (GUI).
  • GUI graphical user interface
  • the image analysis is anonymous, i.e., a unique identifier assigned to a subject created through joints analysis does not identify personal identification details (such as names, email addresses, mailing addresses, credit card numbers, bank account numbers, driver’s license number, etc.) of any specific subject in the real space.
  • a number of flowcharts illustrating logic are described herein.
  • the logic can be implemented using processors configured as described above programmed using computer programs stored in memory accessible and executable by the processors, and in other configurations, by dedicated logic hardware, including field programmable integrated circuits, and by combinations of dedicated logic hardware and computer programs.
  • all flowcharts herein it will be appreciated that many of the steps can be combined, performed in parallel, or performed in a different sequence, without affecting the functions achieved. In some cases, as the reader will appreciate, a rearrangement of steps will achieve the same results only if certain other changes are made as well. In other cases, as the reader will appreciate, a rearrangement of steps will achieve the same results only if certain conditions are satisfied.
  • the flow charts herein show only steps that are pertinent to an understanding of the embodiments, and it will be understood that numerous additional steps for accomplishing other functions can be performed before, after and between those shown.
  • Fig. 9 is a flowchart illustrating process steps for tracking subjects.
  • the process starts at step 902.
  • the cameras 114 having field of view in an area of the real space are calibrated in process step 904.
  • Video processes are performed at step 906 by image recognition engines 1 l2a-l 12h.
  • the video process is performed per camera to process batches of image frames received from respective cameras.
  • the output of all video processes from respective image recognition engines 1 l2a-l 12h are given as input to a scene process performed by the tracking engine 110 at step 908.
  • the scene process identifies new subjects and updates the joint locations of existing subjects.
  • it is checked if there are more image frames to be processed. If there are more image frames, the process continues at step 906, otherwise the process ends at step 914.
  • a subject is introduced in the real space to identify conjugate pairs of corresponding points between cameras with overlapping fields of view. Some details of this process are described above. The process is repeated for every pair of overlapping cameras at step 1012. The process ends if there are no more cameras (step 1014).
  • k-contiguously timestamped images per camera are selected as a batch for further processing.
  • the value of k 6 which is calculated based on available memory for the video process in the network nodes lOla-lOln, respectively hosting image recognition engines 112a- 112h.
  • the size of images is set to appropriate dimensions. In one embodiment, the images have a width of 1280 pixels, height of 720 pixels and three channels RGB (representing red, green and blue colors).
  • a plurality of trained convolutional neural networks (CNN) process the images and generate arrays of joints data structures per image.
  • the output of the CNNs are arrays of joints data structures per image (step 1108). This output is sent to a scene process at step 1110.
  • the scene process 1415 produces an output 1457 comprising a list of all subjects in the real space at a moment in time.
  • the list includes a key-value dictionary per subject.
  • the key is a unique identifier of a subject and the value is another key-value dictionary with the key as the frame number and the value as the camera-subject joint key-value dictionary.
  • the camera- subject joint key-value dictionary is a per subject dictionary in which the key is the camera identifier and the value is a list of joints.
  • FIG. 15A An architectural level schematic of a system in accordance with an implementation. Because Fig. 15A is an architectural diagram, certain details are omitted to improve the clarity of the description.
  • Fig. 15 A is a high-level architecture of pipelines of convolutional neural networks
  • multi-CNN pipelines processing image frames received from cameras 114 to generate shopping cart data structures for each subject in the real space.
  • the system described here includes per camera image recognition engines as described above for identifying and tracking multi -joint subjects. Alternative image recognition engines can be used, including examples in which only one“joint” is recognized and tracked per individual, or other features or other types of images data over space and time are utilized to recognize and track subjects in the real space being processed.
  • the multi-CNN pipelines run in parallel per camera, moving images from respective cameras to image recognition engines H2a-l l2n via circular buffers 1502 per camera.
  • the image frames corresponding to sequences of images from each camera are sent at the rate of 30 frames per second (fps) to respective image recognition engines 1 l2a-l 12h.
  • Each image frame has a timestamp, identity of the camera (abbreviated as “camera id”), and a frame identity (abbreviated as“frame id”) along with the image data.
  • the image frames are stored in a circular buffer 1502 (also referred to as a ring buffer) per camera 114.
  • Circular buffers 1502 store a set of consecutively timestamped image frames from respective cameras 114.
  • the bounding box generator 1504 implements the logic to process the data sets to specify bounding boxes which include images of hands of identified subjects in images in the sequences of images.
  • the bounding box generator 1504 identifies locations of hand joints in each source image frame per camera using locations of hand joints in the multi -joints data structures 800 corresponding to the respective source image frame.
  • the bounding box generator maps the joint locations from 3D real space coordinates to 2D coordinates in the image frames of respective source images.
  • the bounding box generator 1504 creates bounding boxes for hand joints in image frames in a circular buffer per camera 114.
  • the bounding box is a 128 pixels (width) by 128 pixels (height) portion of the image frame with the hand joint located in the center of the bounding box.
  • the size of the bounding box is 64 pixels x 64 pixels or 32 pixels x 32 pixels.
  • the hand locations of subjects are inferred from locations of elbow and wrist joints. For example, the right hand location of a subject is extrapolated using the location of the right elbow (identified as pl) and the right wrist (identified as p2) as
  • the WhatCNN 1506 identifies whether the hand joint is empty.
  • the WhatCNN 1506 also identifies a SKU (stock keeping unit) number of the inventory item in the hand joint, a confidence value indicating the item in the hand joint is a non-SKU item (i.e. it does not belong to the shopping store inventory) and a context of the hand joint location in the image frame.
  • SKU stock keeping unit
  • the outputs of WhatCNN models 1506 for all cameras 114 are processed by a single WhenCNN model 1508 for a pre-determined window of time.
  • the WhenCNN 1508 performs time series analysis for both hands of subjects to identify whether a subject took a store inventory item from a shelf or put a store inventory item on a shelf.
  • a shopping cart data structure 1510 (also referred to as a log data structure including a list of inventory items) is created per subject to keep a record of the store inventory items in a shopping cart (or basket) associated with the subject.
  • the second image processors subsystem 2604 receives the same data sets comprising subjects identified by joints data structures 800 and corresponding image frames from sequences of image frames per camera as given input to the third image processors.
  • the subsystem 2604 includes background image recognition engines, recognizing semantically significant differences in the background (i.e. inventory display structures like shelves) as they relate to puts and takes of inventory items for example, over time in the images from each camera.
  • a selection logic component (not shown in Fig. 15A) uses a confidence score to select output from either the second image processors or the third image processors to generate the shopping cart data structure 1510.
  • FIG. 15A is presented in Figs. 16, 17, and 18.
  • the system tracks puts and takes of inventory items by subjects in an area of real space.
  • the area of real space is the shopping store with inventory items placed in shelves organized in aisles as shown in Figs. 2 and 3.
  • shelves containing inventory items can be organized in a variety of different arrangements.
  • shelves can be arranged in a line with their back sides against a wall of the shopping store and the front side facing towards an open area in the real space.
  • a plurality of cameras 114 with overlapping fields of view in the real space produce sequences of images of their corresponding fields of view.
  • the field of view of one camera overlaps with the field of view of at least one other camera as shown in Figs. 2 and 3. Joints CNN - Identification and Update of Subjects
  • Fig. 16 is a flowchart of processing steps performed by joints CNN 112a- 112h to identify subjects in the real space.
  • the subjects are customers moving in the store in aisles between shelves and other open spaces.
  • the process starts at step 1602.
  • the cameras are calibrated before sequences of images from cameras are processed to identify subjects. Details of camera calibration are presented above.
  • Cameras 114 with overlapping fields of view capture images of real space in which subjects are present (step 1604).
  • the cameras are configured to generate synchronized sequences of images.
  • the sequences of images of each camera are stored in respective circular buffers 1502 per camera.
  • Joints CNNs 1 l2a-l 12h receive sequences of image frames from corresponding cameras 114 (step 1606).
  • Each joints CNN processes batches of images from a corresponding camera through multiple convolution network layers to identify j oints of subjects in image frames from corresponding camera.
  • the architecture and processing of images by an example convolutional neural network is presented Fig. 5.
  • the joints of a subject are identified by more than one joints-CNN.
  • the two dimensional (2D) coordinates of joints data structures 600 produced by joints-CNN are mapped to three dimensional (3D) coordinates of the real space to identify joints locations in the real space. Details of this mapping are presented in discussion of Fig. 7 in which the tracking engine 110 translates the coordinates of the elements in the arrays of joints data structures corresponding to images in different sequences of images into candidate joints having coordinates in the real space.
  • the joints of a subject are organized in two categories (foot joints and non-foot joints) for grouping the joints into constellations, as discussed above.
  • the left and right-ankle joint type in the current example are considered foot joints for the purpose of this procedure.
  • heuristics are applied to assign a candidate left foot joint and a candidate right foot joint to a set of candidate joints to create a subject.
  • the logic to identify sets of candidate joints comprises heuristic functions based on physical relationships among joints of subjects in real space to identify sets of candidate joints as subjects.
  • the existing subjects are updated using the corresponding non-foot joints. If there are more images for processing (step 1620), steps 1606 to 1618 are repeated, otherwise the process ends at step 1622.
  • a first data sets are produced at the end of the process described above.
  • the first data sets identify subject and the locations of the identified subjects in the real space. In one embodiment, the first data sets are presented above in relation to Fig. 15A as joints data structures 800 per subject. WhatCNN - Classification of Hand Joints
  • Fig. 17 is a flowchart illustrating processing steps to identify inventory items in hands of subjects identified in the real space.
  • the subjects are customers in the shopping store. As the customers move in the aisles and opens spaces, they pick up inventory items stocked in the shelves and put the items in their shopping cart or basket.
  • the image recognition engines identify subjects in the sets of images in the sequences of images received from the plurality of cameras.
  • the system includes the logic to process sets of images in the sequences of images that include the identified subjects to detect takes of inventory items by identified subjects and puts of inventory items on the shelves by identified subjects.
  • the logic to process sets of images includes, for the identified subjects, logic to process images to generate classifications of the images of the identified subjects.
  • the logic to process sets of images includes, for the identified subjects, logic to identify bounding boxes of data representing hands in images in the sets of images of the identified subjects.
  • the data in the bounding boxes is processed to generate classifications of data within the bounding boxes for the identified subjects.
  • the classifications include whether the identified subject is holding an inventory item.
  • the classifications include a first nearness classification indicating a location of a hand of the identified subject relative to a shelf.
  • the classifications include a second nearness classification indicating a location of a hand of the identified subject relative to a body of the identified subject.
  • the classifications include a third nearness classification indicating a location of a hand of the identified subject relative to a basket associated with an identified subject.
  • the classifications include an identifier of a likely inventory item.
  • the process starts at step 1702.
  • locations of hands (represented by hand joints) of subjects in image frames are identified.
  • the bounding box generator 1504 identifies hand locations of subjects per frame from each camera using joint locations identified in the first data sets generated by joints CNNs 1 l2a-l 12h as described in Fig. 18.
  • the bounding box generator 1504 processes the first data sets to specify bounding boxes which include images of hands of identified multi -joint subjects in images in the sequences of images. Details of bounding box generator are presented above in discussion of Fig. 15 A.
  • a second image recognition engine receives sequences of images from the plurality of cameras and processes the specified bounding boxes in the images to generate a classification of hands of the identified subjects (step 1708).
  • each of the image recognition engines used to classify the subjects based on images of hands comprises a trained convolutional neural network referred to as a WhatCNN 1506.
  • WhatCNNs are arranged in multi-CNN pipelines as described above in relation to Fig. 15 A.
  • the input to a WhatCNN is a multi-dimensional array BxWxHxC (also referred to as a BxWxHxC tensor).
  • the foreground mask, forearm mask and upperarm mask are additional and optional input data sources for the WhatCNN in this example, which the CNN can include in the processing to classify information in the RGB image data.
  • the foreground mask can be generated using mixture of Gaussian algorithms, for example.
  • the forearm mask can be a line between the wrist and elbow providing context produced using information in the Joints data structure .
  • the upperarm mask can be a line between the elbow and shoulder produced using information in the Joints data structure.
  • Different values of B, W, H and C parameters can be used in other embodiments.
  • the size of the bounding boxes is larger e.g., 64 pixels (width) by 64 pixels (height) or 128 pixels (width) by 128 pixels (height).
  • the classifications in this example further include a third nearness classification indicating a location of a hand of the identified subject relative to a basket associated with an identified subject, where a“basket” in this context is a bag, a basket, a cart or other object used by the subject to hold the inventory items during shopping.
  • the classifications include an identifier of a likely inventory item.
  • the final layer of the WhatCNN 1506 produces logits which are raw values of predictions. The logits are represented as floating point values and further processed, as described below, for generating a classification result. In one embodiment, the outputs of the
  • the output“L” per image frame is a raw activation from the WhatCNN 1506.
  • Logits“L” are processed at step 1710 to identify inventory item and context.
  • the first“N” logits represent confidence that the subject is holding one of the“N” inventory items.
  • Logits“L” include an additional five (5) logits which are explained below.
  • the first logit represents confidence that the image of the item in hand of the subject is not one of the store SKU items (also referred to as non-SKU item).
  • the second logit indicates a confidence whether the subject is holding an item or not.
  • a large positive value indicates that WhatCNN model has a high level of confidence that the subject is holding an item.
  • a large negative value indicates that the model is confident that the subject is not holding any item.
  • a close to zero value of the second logit indicates that WhatCNN model is not confident in predicting whether the subject is holding an item or not.
  • the next three logits represent first, second and third nearness classifications, including a first nearness classification indicating a location of a hand of the identified subject relative to a shelf, a second nearness classification indicating a location of a hand of the identified subject relative to a body of the identified subject, and a third nearness classification indicating a location of a hand of the identified subject relative to a basket associated with an identified subject.
  • the three logits represent context of the hand location with one logit each indicating confidence that the context of the hand is near to a shelf, near to a basket (or a shopping cart), or near to a body of the subject.
  • the WhatCNN is trained using a training dataset containing hand images in the three contexts: near to a shelf, near to a basket (or a shopping cart), and near to a body of a subject.
  • a“nearness” parameter is used by the system to classify the context of the hand. In such an embodiment, the system determines the distance of a hand of the identified subject to the shelf, basket (or a shopping cart), and body of the subject to classify the context.
  • the output of a WhatCNN is“L” logits comprised of N SKU logits, 1 Non-SKU logit, 1 holding logit, and 3 context logits as described above.
  • the SKU logits (first N logits) and the non-SKU logit (the first logit following the N logits) are processed by a softmax function.
  • the softmax function transforms a -dimensional vector of arbitrary real values to a -dimensional vector of real values in the range [0, 1] that add up to L
  • a softmax function calculates the probabilities distribution of the item over N + 1 items.
  • the output values are between 0 and 1, and the sum of all the probabilities equals one.
  • the system implements logic to perform time sequence analysis over the classifications of subjects to detect takes and puts by the identified subjects based on foreground image processing of the subjects.
  • the time sequence analysis identifies gestures of the subjects and inventory items associated with the gestures represented in the sequences of images.
  • the outputs of WhatCNNs 1506 in the multi-CNN pipelines are given as input to the WhenCNN 1508 which processes these inputs to detect takes and puts by the identified subjects.
  • the system includes logic, responsive to the detected takes and puts, to generate a log data structure including a list of inventory items for each identified subject.
  • the log data structure is also referred to as a shopping cart data structure 1510 per subject.
  • the above data structure is generated for each hand in an image frame and also includes data about the other hand of the same subject. For example, if data is for the left hand joint of a subject, corresponding values for the right hand are included as“other” logits.
  • the fifth logit (item number 3 in the list above referred to as log_sku) is the log of SKU logit in“L” logits described above.
  • the sixth logit is the log of SKU logit for other hand.
  • A“roll” function generates the same information before and after the current frame. For example, the seventh logit (referred to as roll(log_sku, -30)) is the log of the SKU logit, 30 frames earlier than the current frame.
  • the eighth logit is the log of the SKU logits for the hand, 30 frames later than the current frame.
  • the ninth and tenth data values in the list are similar data for the other hand 30 frames earlier and 30 frames later than the current frame.
  • a similar data structure for the other hand is also generated, resulting in a total of 20 logits per subject per image frame per camera.
  • the batch of image frames (64) can be imagined as a smaller window of image frames placed in the middle of a larger window of image frame 110 with additional image frames for forward and backward search on both sides.
  • the input BxCxTxCams to WhenCNN 1508 is composed of 20 logits for both hands of subjects identified in batch“B” of image frames from all cameras 114 (referred to as“Cams”).
  • the consolidated input is given to a single trained convolutional neural network referred to as WhenCNN model 1508.
  • the output of the WhenCNN model comprises of 3 logits, representing confidence in three possible actions of an identified subject: taking an inventory item from a shelf, putting an inventory item back on the shelf, and no action.
  • the three output logits are processed by a softmax function to predict an action performed.
  • the three classification logits are generated at regular intervals for each subject and results are stored per person along with a time stamp. In one embodiment, the three logits are generated every twenty frames per subject.
  • a window of 110 image frames is formed around the current image frame.
  • a time series analysis of these three logits per subject over a period of time is performed (step 1808) to identify gestures corresponding to true events and their time of occurrence.
  • a non-maximum suppression (NMS) algorithm is used for this purpose.
  • NMS non-maximum suppression
  • As one event i.e. put or take of an item by a subject
  • WhenCNN 1508 multiple times both from the same camera and from multiple cameras, the NMS removes superfluous events for a subject.
  • NMS is a rescoring technique comprising two main tasks:“matching loss” that penalizes superfluous detections and“joint processing” of neighbors to know if there is a better detection close-by.
  • the true events of takes and puts for each subject are further processed by calculating an average of the SKU logits for 30 image frames prior to the image frame with the true event. Finally, the arguments of the maxima (abbreviated arg max or argmax) is used to determine the largest value.
  • the inventory item classified by the argmax value is used to identify the inventory item put or take from the shelf.
  • the inventory item is added to a log of SKUs (also referred to as shopping cart or basket) of respective subjects in step 1810.
  • the process steps 1804 to 1810 are repeated, if there is more classification data (checked at step 1812). Over a period of time, this processing results in updates to the shopping cart or basket of each subject. The process ends at step 1814.
  • Fig. 19 presents an embodiment of the system in which data from scene process
  • the output from the scene process 1415 is a joints dictionary.
  • keys are unique joint identifiers and values are unique subject identifiers with which the joint is associated. If no subject is associated with a joint, then it is not included in the dictionary.
  • Each video process 1411 receives a joints dictionary from the scene process and stores it into a ring buffer that maps frame numbers to the returned dictionary. Using the returned key -value dictionary, the video processes select subsets of the image at each moment in time that are near hands associated with identified subjects. These portions of image frames around hand joints can be referred to as region proposals.
  • a region proposal is the frame image of hand location from one or more cameras with the subject in their corresponding fields of view.
  • a region proposal is generated by every camera in the system. It includes empty hands as well as hands carrying shopping store inventory items and items not belonging to shopping store inventory.
  • Video processes select portions of image frames containing hand joint per moment in time. Similar slices of foreground masks are generated. The above (image portions of hand joints and foreground masks) are concatenated with the joints dictionary (indicating subjects to whom respective hand joints belong) to produce a multi-dimensional array. This output from video processes is given as input to the WhatCNN model.
  • the classification results of the WhatCNN model are stored in the region proposal data structures (produced by video processes). All regions for a moment in time are then given back as input to the scene process.
  • the scene process stores the results in a key-value dictionary, where the key is a subject identifier and the value is a key-value dictionary, where the key is a camera identifier and the value is a region's logits.
  • This aggregated data structure is then stored in a ring buffer that maps frame numbers to the aggregated structure for each moment in time.
  • Fig. 20 presents an embodiment of the system in which the WhenCNN 1508 receives output from a scene process following the hand image classifications performed by the WhatCNN models per video process as explained in Fig. 19.
  • Region proposal data structures for a period of time e.g., for one second, are given as input to the scene process.
  • the input includes 30 time periods and corresponding region proposals.
  • the scene process reduces 30 region proposals (per hand) to a single integer representing the inventory item SKU.
  • the output of the scene process is a key-value dictionary in which the key is a subject identifier and the value is the SKU integer.
  • the WhenCNN model 1508 performs a time series analysis to determine the evolution of this dictionary over time. This results in identification of items taken from shelves and put on shelves in the shopping store.
  • the output of the WhenCNN model is a key -value dictionary in which the key is the subject identifier and the value is logits produced by the
  • a set of heuristics 2002 is used to determine the shopping cart data structure 1510 per subject.
  • the heuristics are applied to the output of the WhenCNN, joint locations of subjects indicated by their respective joints data structures, and planograms.
  • the planograms are precomputed maps of inventory items on shelves.
  • the heuristics 2002 determine, for each take or put, whether the inventory item is put on a shelf or taken from a shelf, whether the inventory item is put in a shopping cart (or a basket) or taken from the shopping cart (or the basket) or whether the inventory item is close to the identified subject’s body.
  • Fig. 21 presents an example architecture of WhatCNN model 1506.
  • the dimensionality of different layers in terms of their respective width (in pixels), height (in pixels) and number of channels is also presented.
  • the first convolutional layer 2113 receives input 2111 and has a width of 64 pixels, height of 64 pixels and has 64 channels (written as 64x64x64).
  • the details of input to the WhatCNN are presented above.
  • the direction of arrows indicates flow of data from one layer to the following layer.
  • the second convolutional layer 2115 has a dimensionality of 32x32x64.
  • the five additional logits include the first logit representing confidence that item in the image is a non-SKU item, and the second logit representing confidence whether the subject is holding an item.
  • the next three logits represent first, second and third nearness classifications, as described above.
  • the final output of the WhatCNN is shown at 2137.
  • the example architecture uses batch
  • BN convolutional neural network
  • Figs. 22, 23, and 24 are graphical visualizations of different parts of an implementation of WhatCNN 1506.
  • the figures are adapted from graphical visualizations of a WhatCNN model generated by TensorBoardTM.
  • TensorBoardTM is a suite of visualization tools for inspecting and understanding deep learning models e.g., convolutional neural networks.
  • Fig. 22 shows a high level architecture of the convolutional neural network model that detects a single hand (“single hand” model 2210).
  • WhatCNN model 1506 comprises two such convolutional neural networks for detecting left and right hands, respectively.
  • the architecture includes four blocks referred to as blockO 2216, blockl 2218, block2 2220, and block3 2222.
  • a block is a higher-level abstraction and comprises multiple nodes representing convolutional layers.
  • the blocks are arranged in a sequence from lower to higher such that output from one block is input to a successive block.
  • the architecture also includes a pooling layer 2214 and a convolution layer 2212. In between the blocks, different non-linearities can be used. In the illustrated embodiment, a ReLU non-linearity is used as described above.
  • the input to the single hand model 2210 is a
  • the output of the single hand model 2210 is combined with a second single hand model and passed to a fully connected network.
  • the output of the single hand model 2210 is compared with ground truth.
  • a prediction error calculated between the output and the ground truth is used to update the weights of convolutional layers.
  • stochastic gradient descent SGD is used for training WhatCNN 1506.
  • FIG. 23 presents further details of the blockO 2216 of the single hand
  • convolutional neural network model of Fig. 22 It comprises four convolutional layers labeled as convO in box 2310, convl 2318, conv2 2320, and conv3 2322. Further details of the
  • convolutional layer convO are presented in the box 2310.
  • the input is processed by a
  • the output of the convolutional layer is processed by a batch normalization layer 2314.
  • ReLU non-linearity 2316 is applied to the output of the batch normalization layer 2314.
  • the output of the convolutional layer convO is passed to the next layer convl 2318.
  • the output of the final convolutional layer conv3 is processed through an addition operation 2324. This operation sums the output from the layer conv3 2322 to unmodified input coming through a skip connection 2326. It has been shown by He et al.
  • Fig. 21 As described in Fig. 21, the output of convolutional layers of a WhatCNN is processed by a fully connected layer. The outputs of two single hand models 2210 are combined and passed as input to a fully connected layer.
  • Fig. 24 is an example implementation of a fully connected layer (FC) 2410. The input to the FC layer is processed by a reshape operator 2412.
  • the reshape operator changes the shape of the tensor before passing it to a next layer 2420.
  • Reshaping includes flattening the output from the convolutional layers i.e., reshaping the output from a multi-dimensional matrix to a one-dimensional matrix or a vector.
  • the output of the reshape operator 2412 is passed to a matrix multiplication operator labelled as MatMul 2422.
  • the output from the MatMul 2422 is passed to a matrix plus addition operator labelled as xw_plus_b 2424.
  • the operator 2424 For each input“x”, the operator 2424 multiplies the input by a matrix“w” and a vector“b” to produce the output“w” is a trainable parameter associated with the input“x” and“b” is another trainable parameter which is called bias or intercept.
  • a training data set of images of hands holding different inventory items in different contexts, as well as empty hands in different contexts is created.
  • human actors hold each unique SKU inventory item in multiple different ways, at different locations of a test environment.
  • the context of their hands range from being close to the actor's body, being close to the store's shelf, and being close to the actor’s shopping cart or basket.
  • the actor performs the above actions with an empty hand as well. This procedure is completed for both left and right hands. Multiple actors perform these actions simultaneously in the same test environment to simulate the natural occlusion that occurs in real shopping stores.
  • Cameras 114 takes images of actors performing the above actions. In one embodiment, twenty cameras are used in this process.
  • the joints CNNs 1 l2a-l 12h and the tracking engine 110 process the images to identify joints.
  • the bounding box generator 1504 creates bounding boxes of hand regions similar to production or inference. Instead of classifying these hand regions via the WhatCNN 1506, the images are saved to a storage disk. Stored images are reviewed and labelled. An image is assigned three labels: the inventory item SKU, the context, and whether the hand is holding something or not. This process is performed for a large number of images (up to millions of images).
  • the image files are organized according to data collection scenes. The naming convention for image file identifies content and context of the images. Fig.
  • a first part of the file name referred to by a numeral 2502 identifies the data collection scene and also includes the timestamp of the image.
  • a second part 2504 of the file name identifies the source camera. In the example shown in Fig. 25, the image is captured by“camera 4”.
  • a third part 2506 of the file name identifies the frame number from the source camera. In the illustrated example, the file name indicates it is the 94,600 th image frame from camera 4.
  • a fourth part 2508 of the file name identifies ranges of x and y coordinates region in the source image frame from which this hand region image is taken.
  • the region is defined between x coordinate values from pixel 117 to 370 and y coordinates values from pixels 370 and 498.
  • a fifth part 2510 of the file name identifies the person id of the actor in the scene. In the illustrated example, the person in the scene has an id “3”.
  • stochastic gradient descent is used for training WhatCNN 1506.
  • 64 images are randomly selected from the training data and augmented.
  • the purpose of image augmentation is to diversify the training data resulting in better performance of models.
  • the image augmentation includes random flipping of the image, random rotation, random hue shifts, random Gaussian noise, random contrast changes, and random cropping.
  • the amount of augmentation is a hyperparameter and is tuned through hyperparameter search.
  • the augmented images are classified by WhatCNN 1506 during training. The classification is compared with ground truth and coefficients or weights of WhatCNN 1506 are updated by calculating gradient loss function and multiplying the gradient with a learning rate.
  • the above process is repeated many times (e.g., approximately 1000 times) to form an epoch. Between 50 to 200 epochs are performed. During each epoch, the learning rate is slightly decreased following a cosine annealing schedule. Training of WhenCNN Model
  • Training of WhenCNN 1508 is similar to the training of WhatCNN 1506 described above, using backpropagations to reduce prediction error.
  • Actors perform a variety of actions in the training environment.
  • the training is performed in a shopping store with shelves stocked with inventory items.
  • actions performed by actors include, take an inventory item from a shelf, put an inventory item back on a shelf, put an inventory item into a shopping cart (or a basket), take an inventory item back from the shopping cart, swap an item between left and right hands, put an inventory item into the actor’s nook.
  • a nook refers to a location on the actor’s body that can hold an inventory item besides the left and right hands.
  • Some examples of nook include, an inventory item squeezed between a forearm and upper arm, squeezed between a forearm and a chest, squeezed between neck and a shoulder.
  • the cameras 114 record videos of all actions described above during training.
  • the videos are reviewed and all image frames are labelled indicating the timestamp and the action performed. These labels are referred to as action labels for respective image frames.
  • the image frames are processed through the multi-CNN pipelines up to the WhatCNNs 1506 as described above for production or inference.
  • the output of WhatCNNs along withthe associated action labels are then used to train the WhenCNN 1508, with the action labels acting as ground truth.
  • Stochastic gradient descent (SGD) with a cosine annealing schedule is used for training as described above for training of WhatCNN 1506.
  • temporal augmentation is also applied to image frames during training of the WhenCNN.
  • Some examples include mirroring, adding Gaussian noise, swapping the logits associated with left and right hands, shortening the time, shortening the time series by dropping image frames, lengthening the time series by duplicating frames, and dropping the data points in the time series to simulate spottiness in the underlying model generating input for the WhenCNN.
  • Mirroring includes reversing the time series and respective labels, for example a put action becomes a take action when reversed.
  • Fig. 26 presents a high level schematic of a system in accordance with an implementation. Because Fig. 26 is an architectural diagram, certain details are omitted to improve the clarity of description.
  • the system presented in Fig. 26 receives image frames from a plurality of cameras 114.
  • the cameras 114 can be synchronized in time with each other, so that images are captured at the same time, or close in time, and at the same image capture rate. Images captured in all the cameras covering an area of real space at the same time, or close in time, are synchronized in the sense that the synchronized images can be identified in the processing engines as representing different views at a moment in time of subjects having fixed positions in the real space.
  • the cameras 114 are installed in a shopping store (such as a supermarket) such that sets of cameras (two or more) with overlapping fields of view are positioned over each aisle to capture images of real space in the store. There are“n” cameras in the real space. Each camera produces a sequence of images of real space corresponding to its respective field of view.
  • a subject identification subsystem 2602 processes image frames received from cameras 114 to identify and track subjects in the real space.
  • the first image processors include subject image recognition engines.
  • the subject image recognition engines receive corresponding sequences of images from the plurality of cameras, and process images to identify subjects represented in the images in the corresponding sequences of images.
  • the system includes per camera image recognition engines as described above for identifying and tracking multi-joint subjects.
  • Alternative image recognition engines can be used, including examples in which only one“joint” is recognized and tracked per individual, or other features or other types of images data over space and time are utilized to recognize and track subjects in the real space being processed.
  • A“semantic diffing” subsystem 2604 includes background image recognition engines, receiving corresponding sequences of images from the plurality of cameras and recognize semantically significant differences in the background (i.e. inventory display structures like shelves) as they relate to puts and takes of inventory items for example, over time in the images from each camera.
  • the second image processors receive output of the subject identification subsystem 2602 and image frames from cameras 114 as input.
  • the second image processors mask the identified subjects in the foreground to generate masked images.
  • the masked images are generated by replacing bounding boxes that correspond with foreground subjects with background image data.
  • the background image recognition engines process the masked images to identify and classify background changes represented in the images in the corresponding sequences of images.
  • the background image recognition engines comprise convolutional neural networks.
  • the second image processors process identified background changes to make a first set of detections of takes of inventory items by identified subjects and of puts of inventory items on inventory display structures by identified subjects.
  • the first set of detections are also referred to as background detections of puts and takes of inventory items.
  • the first detections identify inventory items taken from the shelves or put on the shelves by customers or employees of the store.
  • the semantic diffing subsystem includes the logic to associate identified background changes with identified subjects.
  • a region proposals subsystem 2606 include foreground image recognition engines, receiving corresponding sequences of images from the plurality of cameras 114, and recognize semantically significant objects in the foreground (i.e. shoppers, their hands and inventory items) as they relate to puts and takes of inventory items for example, over time in the images from each camera.
  • the subsystem 2606 also receives output of the subject identification subsystem 2602.
  • the third image processors process sequences of images from cameras 114 to identify and classify foreground changes represented in the images in the corresponding sequences of images.
  • the third image processors process identified foreground changes to make a second set of detections of takes of inventory items by identified subjects and of puts of inventory items on inventory display structures by identified subjects.
  • the second set of detections are also referred to as foreground detection of puts and takes of inventory items. In the example of a shopping store, the second set of detections identify takes of inventory items and puts of inventory items on inventory display structures by customers and employees of the store.
  • the system described in Fig. 26 includes a selection logic component 2608 to process the first and second sets of detections to generate log data structures including lists of inventory items for identified subjects. For a take or put in the real space, the selection logic 2608 selects the output from either the semantic diffing subsystem 2604 or the region proposals subsystem 2606. In one embodiment, the selection logic 2608 uses a confidence score generated by the semantic diffing subsystem for the first set of detections and a confidence score generated by the region proposals subsystem for a second set of detections to make the selection. The output of the subsystem with a higher confidence score for a particular detection is selected and used to generate a log data structure 1510 (also referred to as a shopping cart data structure) including a list of inventory items associated with identified foreground subjects.
  • a log data structure 1510 also referred to as a shopping cart data structure
  • Fig. 27 presents subsystem components implementing the system for tracking changes by subjects in an area of real space.
  • the system comprises of the plurality of cameras 114 producing respective sequences of images of corresponding fields of view in the real space.
  • the field of view of each camera overlaps with the field of view of at least one other camera in the plurality of cameras as described above.
  • the sequences of image frames corresponding to the images produced by the plurality of cameras 114 are stored in a circular buffer 1502 (also referred to as a ring buffer) per camera 114.
  • Each image frame has a timestamp, identity of the camera (abbreviated as“camera id”), and a frame identity
  • Circular buffers 1502 store a set of consecutively timestamped image frames from respective cameras 114.
  • the cameras 114 are configured to generate synchronized sequences of images.
  • a background image store 2704 in the semantic diffing subsystem 2604, stores masked images (also referred to as background images in which foreground subjects have been removed by masking) for corresponding sequences of images from cameras 114.
  • the background image store 2704 is also referred to as a background buffer.
  • the size of the masked images is the same as the size of image frames in the circular buffer 1502.
  • a masked image is stored in the background image store 2704 corresponding to each image frame in the sequences of image frames per camera.
  • the semantic diffing subsystem 2604 (or the second image processors) includes a mask generator 2724 producing masks of foreground subjects represented in the images in the corresponding sequences of images from a camera.
  • a mask generator 2724 produces masks of foreground subjects represented in the images in the corresponding sequences of images from a camera.
  • one mask generator processes sequences of images per camera.
  • the foreground subjects are customers or employees of the store in front of the background shelves containing items for sale.
  • the joint data structures 800 and image frames from the circular buffer 1502 are given as input to the mask generator 2724.
  • the joint data structures identify locations of foreground subjects in each image frame.
  • the mask generator 2724 generates a bounding box per foreground subject identified in the image frame.
  • the mask generator 2724 uses the values of the x and y coordinates of joint locations in 2D image frame to determine the four boundaries of the bounding box.
  • a minimum value of x (from all x values of joints for a subject) defines the left vertical boundary of the bounding box for the subject.
  • a minimum value of y defines the bottom horizontal boundary of the bounding box.
  • the mask generator 2724 produces bounding boxes for foreground subjects using a convolutional neural network-based person detection and localization algorithm. In such an embodiment, the mask generator 2724 does not use the joint data structures 800 to generate bounding boxes for foreground subjects.
  • the semantic diffing subsystem 2604 (or the second image processors) include a mask logic to process images in the sequences of images to replace foreground image data representing the identified subjects with background image data from the background images for the corresponding sequences of images to provide the masked images, resulting in a new background image for processing.
  • the mask logic processes images in the sequences of images to replace foreground image data defined by the image masks with background image data.
  • the background image data is taken from the background images for the corresponding sequences of images to generate the corresponding masked images.
  • a background image in the background image store 2704 is the same as its corresponding image frame in the sequences of images per camera.
  • the mask generator 2724 creates a bounding box of the customer and sends it to a mask logic component 2702.
  • the mask logic component 2702 combines, such as by averaging or summing by pixel, sets of N masked images in the sequences of images to generate sequences of factored images for each camera.
  • the second image processors identify and classify background changes by processing the sequence of factored images.
  • a factored image can be generated, for example, by taking an average value for pixels in the N masked images in the sequence of masked images per camera.
  • the value of N is equal to the frame rate of cameras 114, for example if the frame rate is 30 FPS (frames per second), the value of N is 30.
  • the masked images for a time period of one second are combined to generate a factored image. Taking the average pixel values minimizes the pixel fluctuations due to sensor noise and luminosity changes in the area of real space.
  • the second image processors identify and classify background changes by processing the sequence of factored images.
  • a factored image in the sequences of factored images is compared with the preceding factored image for the same camera by a bit mask calculator 2710. Pairs of factored images 2706 are given as input to the bit mask calculator 2710 to generate a bit mask identifying changes in corresponding pixels of the two factored images.
  • the bit mask has ls at the pixel locations where the difference between the corresponding pixels’ (current and previous factored image) RGB (red, green and blue channels) values is greater than a“difference threshold”.
  • the value of the difference threshold is adjustable. In one embodiment, the value of the difference threshold is set at 0.1.
  • the bit mask and the pair of factored images (current and previous) from sequences of factored images per camera are given as input to background image recognition engines.
  • the background image recognition engines comprise convolutional neural networks and are referred to as ChangeCNN 27l4a-27l4n.
  • a single ChangeCNN processes sequences of factored images per camera.
  • the masked images from corresponding sequences of images are not combined.
  • the bit mask is calculated from the pairs of masked images. In this embodiment, the pairs of masked images and the bit mask is then given as input to the ChangeCNN.
  • the input to a ChangeCNN model in this example consists of seven (7) channels including three image channels (red, green and blue) per factored image and one channel for the bit mask.
  • the ChangeCNN comprises of multiple convolutional layers and one or more fully connected (FC) layers.
  • the ChangeCNN comprises of the same number of convolutional and FC layers as the JointsCNN H2a-l l2n as illustrated in Fig. 5.
  • the background image recognition engines identify and classify changes in the factored images and produce change data structures for the corresponding sequences of images.
  • the change data structures include coordinates in the masked images of identified background changes, identifiers of an inventory item subject of the identified background changes and classifications of the identified background changes.
  • the classifications of the identified background changes in the change data structures classify whether the identified inventory item has been added or removed relative to the background image.
  • the ChangeCNN As multiple items can be taken or put on the shelf simultaneously by one or more subjects, the ChangeCNN generates a number“B” overlapping bounding box predictions per output location.
  • a bounding box prediction corresponds to a change in the factored image.
  • the shopping store has a number“C” unique inventory items, each identified by a unique SKU.
  • the ChangeCNN predicts the SKU of the inventory item subject of the change.
  • the ChangeCNN identifies the change (or inventory event type) for every location (pixel) in the output indicating whether the item identified is taken from the shelf or put on the shelf.
  • the above three parts of the output from ChangeCNN are described by an expression“5 * B + C + 1”.
  • Each bounding box“B” prediction comprises of five (5) numbers, therefore“B” is multiplied by 5. These five numbers represent the“x” and“y” coordinates of the center of the bounding box, the width and height of the bounding box.
  • the fifth number represents
  • ChangeCNN model s confidence score for prediction of the bounding box.“B” is a
  • the value of“B” equals 4.
  • ChangeCNN is then expressed as“W * H * (5 * B + C + 1)”.
  • the bounding box output model is based on object detection system proposed by Redmon and Farhadi in their paper,“YOLO9000: Better, Faster, Stronger” published on December 25, 2016. The paper is available at
  • the outputs of ChangeCNN 27l4a-27l4n corresponding to sequences of images from cameras with overlapping fields of view are combined by a coordination logic component 2718.
  • the coordination logic component processes change data structures from sets of cameras having overlapping fields of view to locate the identified background changes in real space.
  • the coordination logic component 2718 selects bounding boxes representing the inventory items having the same SKU and the same inventory event type (take or put) from multiple cameras with overlapping fields of view.
  • the selected bounding boxes are then triangulated in the 3D real space using triangulation techniques described above to identify the location of the inventory item in 3D real space. Locations of shelves in the real space are compared with the triangulated locations of the inventory items in the 3D real space. False positive predictions are discarded.
  • Triangulated locations of bounding boxes in the 3D real space that map to a shelf are considered true predictions of inventory events.
  • the classifications of identified background changes in the change data structures produced by the second image processors classify whether the identified inventory item has been added or removed relative to the background image.
  • the classifications of identified background changes in the change data structures indicate whether the identified inventory item has been added or removed relative to the background image and the system includes logic to associate background changes with identified subjects. The system makes detections of takes of inventory items by the identified subjects and of puts of inventory items on inventory display structures by the identified subjects.
  • a log generator 2720 implements the logic to associate changes identified by true predictions of changes with identified subjects near the location of the change. In an embodiment utilizing the joints identification engine to identify subjects, the log generator 2720 determines the positions of hand joints of subjects in the 3D real space using joint data structures 800. A subject whose hand joint location is within a threshold distance to the location of a change at the time of the change is identified. The log generator associates the change with the identified subject.
  • N masked images are combined to generate factored images which are then given as input to the ChangeCNN.
  • N equals the frame rate (frames per second) of the cameras 114.
  • the positions of hands of subjects during a one second time period are compared with the location of the change to associate the changes with identified subjects. If more than one subject’s hand joint locations are within the threshold distance to a location of a change, then association of the change with a subject is deferred to output of the foreground image processing subsystem 2606.
  • the foreground image processing (region proposals) subsystem 2606 (also referred to as the third image processors) include foreground image recognition engines receiving images from the sequences of images from the plurality of cameras.
  • the third image processors include logic to identify and classify foreground changes represented in the images in the corresponding sequences of images.
  • the region proposals subsystem 2606 produces a second set of detections of takes of inventory items by the identified subjects and of puts of inventory items on inventory display structures by the identified subjects.
  • the subsystem 2606 includes the bounding box generator 1504, the WhatCNN 1506 and the
  • the system described in Fig. 27 includes the selection logic to process the first and second sets of detections to generate log data structures including lists of inventory items for identified subjects.
  • the first set of detections of takes of inventory items by the identified subjects and of puts of inventory items on inventory display structures by the identified subjects are generated by the log generator 2720.
  • the first set of detections are determined using the outputs of second image processors and the joint data structures 800 as described above.
  • the second set of detections of takes of inventory items by the identified subjects and of puts of inventory items on inventory display structures by the identified subjects are determined using the output of the third image processors.
  • the selection logic controller 2608 selects the output from either the second image processors (semantic diffing subsystem 2604) or the third image processors (region proposals subsystem 2606). In one embodiment, the selection logic selects the output from an image processor with a higher confidence score for prediction of that inventory event.
  • Figs. 28A and 28B present detailed steps performed by the semantic diffing subsystem 2604 to track changes by subjects in an area of real space.
  • the subjects are customers and employees of the store moving in the store in aisles between shelves and other open spaces.
  • the process starts at step 2802.
  • the cameras 114 are calibrated before sequences of images from cameras are processed to identify subjects. Details of camera calibration are presented above. Cameras 114 with overlapping fields of view capture images of real space in which subjects are present.
  • the cameras are configured to generate synchronized sequences of images at the rate of N frames per second.
  • the sequences of images of each camera are stored in respective circular buffers 1502 per camera at step 2804.
  • a circular buffer also referred to as a ring buffer
  • the background image store 2704 is initialized with initial image frame in the sequence of image frames per camera with no foreground subjects (step 2806).
  • bounding boxes per subject are generated using their corresponding joint data structures 800 as described above (step 2808).
  • a masked image is created by replacing the pixels in the bounding boxes per image frame by pixels at the same locations from the background image from the background image store 2704.
  • the masked image corresponding to each image in the sequences of images per camera is stored in the background image store 2704.
  • the zth masked image is used as a background image for replacing pixels in the following (/+ 1 ) image frame in the sequence of image frames per camera.
  • N masked images are combined to generate factored images.
  • a difference heat map is generated by comparing pixel values of pairs of factored images.
  • the difference between pixels at a location (x, y) in a 2D space of the two factored images (fil and fl2) is calculated as shown below in equation 1 :
  • the difference between the pixels at the same x and y locations in the 2D space is determined using the respective intensity values of red, green and blue (RGB) channels as shown in the equation.
  • the above equation gives a magnitude of the difference (also referred to as Euclidean norm) between corresponding pixels in the two factored images.
  • the difference heat map can contain noise due to sensor noise and luminosity changes in the area of real space.
  • a bit mask is generated for a difference heat map. Semantically meaningful changes are identified by clusters of ls (ones) in the bit mask. These clusters correspond to changes identifying inventory items taken from the shelf or put on the shelf. However, noise in the difference heat map can introduce random ls in the bit mask. Additionally, multiple changes (multiple items take from or put on the shelf) can introduce overlapping clusters of ls.
  • image morphology operations are applied to the bit mask. The image morphology operations remove noise (unwanted ls) and also attempt to separate overlapping clusters of ls. This results in a cleaner bit mask comprising clusters of ls corresponding to semantically meaningful changes.
  • Two inputs are given to the morphological operation.
  • the first input is the bit mask and the second input is called a structuring element or kernel.
  • Two basic morphological operations are“erosion” and“dilation”.
  • a kernel consists of ls arranged in a rectangular matrix in a variety of sizes. Kernels of different shapes (for example, circular, elliptical or cross-shaped) are created by adding 0’s at specific locations in the matrix. Kernels of different shapes are used in image morphology operations to achieve desired results in cleaning bit masks.
  • erosion operation a kernel slides (or moves) over the bit mask.
  • a pixel (either 1 or 0) in the bit mask is considered 1 if all the pixels under the kernel are ls. Otherwise, it is eroded (changed to 0). Erosion operation is useful in removing isolated ls in the bit mask. However, erosion also shrinks the clusters of ls by eroding the edges.
  • Dilation operation is the opposite of erosion. In this operation, when a kernel slides over the bit mask, the values of all pixels in the bit mask area overlapped by the kernel are changed to 1, if value of at least one pixel under the kernel is 1. Dilation is applied to the bit mask after erosion to increase the size clusters of ls. As the noise is removed in erosion, dilation does not introduce random noise to the bit mask. A combination of erosion and dilation operations are applied to achieve cleaner bit masks. For example the following line of computer program code applies a 3x3 filter of ls to the bit mask to perform an“open” operation which applies erosion operation followed by dilation operation to remove noise and restore the size of clusters of ls in the bit mask as described above. The above computer program code uses OpenCV (open source computer vision) library of programming functions for real time computer vision applications. The library is available at https://opencv.org/.
  • OpenCV open source computer vision
  • bit mask cv2.morphologyEx(bit_mask, cv2.MORPH_OPEN, self.kemel_3x3,
  • A“close” operation applies dilation operation followed by erosion operation. It is useful in closing small holes inside the clusters of ls.
  • the following program code applies a close operation to the bit mask using a 30x30 cross-shaped filter.
  • the bit mask and the two factored images are given as input to a convolutional neural network (referred to as ChangeCNN above) per camera.
  • the outputs of ChangeCNN are the change data structures.
  • outputs from ChangeCNNs with overlapping fields of view are combined using triangulation techniques described earlier.
  • a location of the change in the 3D real space is matched with locations of shelves. If location of an inventory event maps to a location on a shelf, the change is considered a true event (step 2824). Otherwise, the change is a false positive and is discarded.
  • True events are associated with a foreground subject.
  • the foreground subject is identified.
  • the joints data structure 800 is used to determine location of a hand joint within a threshold distance of the change. If a foreground subject is identified at the step 2828, the change is associated to the identified subject at a step 2830. If no foreground subject is identified at the step 2828, for example, due to multiple subjects’ hand joint locations within the threshold distance of the change. Then redundant detection of the change by region proposals subsystem is selected at a step 2832. The process ends at a step 2834.
  • a training data set of seven channel inputs is created to train the ChangeCNN.
  • the SKU number for the inventory item in the change is identified and included in the label for the image along with the bounding box.
  • An event type identifying take or put of inventory item is also included in the label of the bounding box.
  • the label for each bounding box identifies, its location on the factored image, the SKU of the item and the event type.
  • a factored image can have more than one bounding boxes. The above process is repeated for every change in all collected factored images in the training data set. A pair of factored images along with the bit mask forms a seven channel input to the ChangeCNN.
  • the ChangeCNN identify and classify background changes represented in the factored images in the corresponding sequences of images in the training data set.
  • the ChangeCNN process identified background changes to make a first set of detections of takes of inventory items by identified subjects and of puts of inventory items on inventory display structures by identified subjects.
  • the output of the ChangeCNN is compared with the ground truth as indicated in labels of training data set.
  • a gradient for one or more cost functions is calculated.
  • the gradient(s) are then propagated to the convolutional neural network (CNN) and the fully connected (FC) neural network so that the prediction error is reduced causing the output to be closer to the ground truth.
  • CNN convolutional neural network
  • FC fully connected
  • a softmax function and a cross-entropy loss function is used for training of the ChangeCNN for class prediction part of the output.
  • the class prediction part of the output includes an SKU identifier of the inventory item and the event type i.e., a take or a put.
  • a second loss function is used to train the ChangeCNN for prediction of bounding boxes.
  • This loss function calculates intersection over union (IOU) between the predicted box and the ground truth box. Area of intersection of bounding box predicted by the ChangeCNN with the true bounding box label is divided by the area of the union of the same bounding boxes. The value of IOU is high if the overlap between the predicted box and the ground truth boxes is large. If more than one predicted bounding boxes overlap the ground truth bounding box, then the one with highest IOU value is selected to calculate the loss function. Details of the loss function are presented by Redmon et. al, in their paper,“You Only Look Once: Unified, Real-Time Object Detection” published on May 9, 2016. The paper is available at
  • the system for tracking puts and takes of inventory items by subjects in an area of real space described above also includes one or more of the following features.
  • a region proposal is the frame image of hand location from all different cameras covering the person.
  • a region proposal is generated by every camera in the system. It includes empty hands as well as hands carrying store items.
  • a region proposal can be used as input to image classification using a deep learning algorithm.
  • This classification engine is called a“WhatCNN” model. It is an in-hand classification model. It classifies the things that are in hands. In-hand image classification can operate even though parts of the object are occluded by the hand. Smaller items may be occluded up to 90% by the hand.
  • the region for image analysis by the WhatCNN model is intentionally kept small in some embodiments because it is computationally expensive.
  • Each camera can have a dedicated GPU. This is performed for every hand image from every camera for every frame.
  • a confidence weight is also assigned to that image (one camera, one point in time).
  • the classification algorithm outputs logits over the entire list of stock keeping units (SKUs) to produce a product and service identification code list of the store for n items and one additional for an empty hand (n+l).
  • SKUs stock keeping units
  • Each video process receives the key-value dictionary from the scene process and stores it into a ring buffer that maps frame numbers to the returned dictionary.
  • the video selects subsets of the image at each moment in time that are near hands associated with known people. These regions are numpy slices. We also take a similar slice around foreground masks and the raw output feature arrays of the Joints CNN. These combined regions are concatenated together into a single multidimensional numpy array and stored in a data structure that holds the numpy array as well as the person ID with which the region is associated and which hand from the person the region came from.
  • a CNN dedicated to classification referred to as a WhatCNN.
  • the output of this CNN is a flat array of floats of size N+l, where N is the number of unique SKUs in the store, and the final class represents the nil class, or empty hand.
  • the floats in this array are referred to as logits.
  • the scene process receives all regions from all videos at a moment in time and stores the results in a key-value dictionary, where the key is a person ID and the value is a key- value dictionary, where the key is a camera ID and the value is a region's logits.
  • This aggregated data structure is then stored in a ring buffer that maps frame numbers to the aggregated structure for each moment in time.
  • the images from different cameras processed by the WhatCNN model are combined over a period of time (multiple cameras over a period of time).
  • An additional input to this model is hand location in 3D space, triangulated from multiple cameras.
  • Another input to this algorithm is the distance of a hand from a planogram of the store. In some embodiments, the planogram can be used to identify if the hand is close to a shelf containing a particular item ( e.g . cheerios boxes).
  • Another input to this algorithm is the foot location on the store.
  • the second classification model uses time series analysis to determine whether the object was picked up from the shelf or placed on the shelf. The images are analyzed over a period of time to make the determination of whether the object that was in the hand in earlier image frames has been put back in the shelf or has been picked up from the shelf.
  • This model also includes output from the shelf model as its input to identify what object this person has picked.
  • the scene process waits for 30 or more aggregated structures to accumulate, representing at least a second of real time, and then performs a further analysis to reduce the aggregated structure down to a single integer for each person ID-hand pair, where the integer is a unique ID representing a SKU in the store. For a moment in time this information is stored in a key-value dictionary where keys are person ID-hand pairs, and values are the SKU integer. This dictionary is stored over time in a ring buffer that maps frame numbers to each dictionary for that moment in time.
  • a further collection of heuristics is then run on the stored results of both the WhenCNN and the stored joint locations of people, as well as a precomputed map of items on the store shelf.
  • This collection of heuristics determines where takes and puts result in items being added to or removed from. For each take/put the heuristics determine if the take or put was from or to a shelf, from or to a basket, or from or to a person.
  • the output is an inventory for each person, stored as an array where the array value at a SKU's index is the number of those SKUs a person has.
  • the system can send the inventory list to the shopper’s phone.
  • the phone displays the user's inventory and asks for confirmation to charge their stored credit card information. If the user accepts, their credit card will be charged.
  • the shopper may also approach an in-store kiosk.
  • the system identifies when the shopper is near the kiosk and will send a message to the kiosk to display the inventory of the shopper.
  • the kiosk asks the shopper to accept the charges for the inventory. If the shopper accepts, they may then swipe their credit card or insert cash to pay.
  • Fig. 16 presents an illustration of the WhenCNN model for region proposals.
  • This feature identifies misplaced items when they are placed back by a person on a random shelf. This causes problems in object identification because the foot and hand location with respect to the planogram will be incorrect. Therefore, the system builds up a modified planogram over time. Based on prior time series analysis, the system is able to determine if a person has placed an item back in the shelf. Next time, when an object is picked up from that shelf location, the system knows that there is at least one misplaced item in that hand location. Correspondingly, the algorithm will have some confidence that the person can pick up the misplaced item from that shelf. If the misplaced item is picked up from the shelf, the system subtracts that item from that location and therefore, the shelf does not have that item anymore. The system can also inform a clerk about a misplaced item via an app so that the clerk can move that item to its correct shelf. 3. Semantic diffing (shelf model)
  • An alternative technology for background image processing comprises a background subtraction algorithm to identify changes to items (items removed or placed) on the shelves. This is based on changes at the pixel level. If there are persons in front of the shelf, then the algorithm stops so that it does not take into account pixel changes due to presence of persons. Background subtraction is a noisy process. Therefore, a cross-camera analysis is conducted. If enough cameras agree that there is a“semantically meaningful” change in the shelf, then the system records that there is a change in that part of the shelf.
  • the next step is to identify whether that change is a“put” or a“get” change.
  • the time series analysis of the second classification model is used.
  • a region proposal for that particular part of the shelf is generated and passed through the deep learning algorithm. This is easier than in-hand image analysis because the object is not occluded inside a hand.
  • a fourth input is given to the algorithm in addition to the three typical RGB inputs.
  • the fourth channel is the background information.
  • the output of the shelf or semantic diffing is input again to the second classification model (time-series analysis model).
  • Semantic diffing in this approach includes the following steps:
  • Images from a camera are compared to earlier images from the same camera.
  • Each corresponding pixel between the two images is compared via a Euclidean distance in RGB space.
  • a collection of image morphology filters are used to remove noise from the marked image.
  • Predefined shopping scripts are used to collect good quality data of images. These images are used for training of algorithms.
  • Data collection includes the following steps:
  • a script is automatically generated telling a human actor what actions to take.
  • actions are randomly sampled from a collection of actions including: take item X, place item X, hold item X for Y seconds.
  • the script serves as an input label to machine learning models (such as the CNNs) that train on the videos of actors.
  • the Store App has several main capabilities; providing data analytic
  • the derivative data is used to perform various kinds of analytics on stores, the shopping experiences they provide, and customer interactions with products, environment, and other people.
  • the data is stored and used in the background to perform analyses of the store and customer interactions.
  • the Store App will display some of the visualizations of this data to retailers. Other data is stored and queried when the data point is desired.
  • the platform visualizes a retailer’s floor plan, shelf layouts, and other store environments with overlays showing levels of various kinds of activity.
  • the platform tracks all of a store’s SKUs. When an item gets put in the incorrect place, the platform will know where that item is and build a log. At some threshold, or immediately, store employees may be alerted to the misplaced item. Alternatively, the staff may access the
  • Misplaced Item Map in the Store App When convenient, staff can then quickly locate and correct misplaced items.
  • Granular breakdown of product interactions such as:
  • impressions will be decided.
  • the threshold is met, the products would make it to her list and be sent to her soon after leaving the store.
  • the shopper could be sent an email a period of time later that offered product(s) on sale or other special information. These products will be items they expressed interest in, but did not purchase.
  • the Shopper App automatically checks people out when they exit the store.
  • the platform does not require shoppers to have or use the Shopper App to use the store.
  • the Shopper App Through use of an app, the Shopper App, the customer can exit the store with merchandise and automatically be charged and given a digital receipt.
  • the shopper must open their app at any time while within the store’s shopping area.
  • the platform will recognize a unique image that is displayed on the shopper’s device.
  • the platform will tie them to their account (Customer Association), and regardless if they keep the app open or not, will be able to remember who they are throughout their time in the store’s shopping area.
  • the Shopper App will display the items in shopper’s
  • the Shopper App also has mapping information on its development roadmap. It can tell a customer where to find items in the store if the customer requests the information by typing in the item being sought. At a later date, we will take a shopper’s shopping list (entered into the app manually or through other intelligent systems) and display the fastest route through the store to collect all the desired items. Other filters, such as‘Bagging Preference’ may be added. The Bagging Preference filter allows a shopper to not follow the fastest route, but to gather sturdier items first, then more fragile items later. 7 Types of customers
  • the system is able to identify if the customer has not paid for the items in the shopping cart, and prompt the checker at the door, before the customer reaches there, to let the checker know about unpaid items.
  • the system can also prompt for one item that has not been paid for, or the system having low confidence about one item. This is referred to as predictive pathfmding.
  • the system assigns color codes (green and yellow) to the customers walking in the store based on the confidence level.
  • the green color coded customers are either logged into the system or the system has a high confidence about them.
  • Yellow color coded customers have one or more items that are not predicted with high confidence.
  • a clerk can look at the yellow dots and click on them to identify problem items, walk up to the customer and fix the problem.
  • a host of analytics information is gathered about the customer such as how much time a customer spent in front of a particular shelf. Additionally, the system tracks the location where a customer is looking (impression on the system), and the items which a customer picked and put back on the shelf. Such analytics are currently available in ecommerce but not available in retail stores.
  • System to generate customer shopping analytics including location-based impressions, directional impressions, A/B analysis, customer recognition, group dynamics etc.
  • the technology described herein can support Cashier-free Checkout. Go to Store.
  • Cashier-free Checkout is a pure machine vision and deep learning based system.
  • Techniques & Capabilities include the following:
  • Ceiling cameras must be installed with double coverage of all areas of the store.
  • An on-premise GPU cluster can fit into one or two server racks in a back office.
  • Example systems can be integrated with or include Point of Sale and Inventory
  • a first system, method and computer program product for capturing arrays of images in stores using synchronized cameras is a first system, method and computer program product for capturing arrays of images in stores using synchronized cameras.
  • a second system, method and computer program product to identify joints in images, and sets of joints of individual persons.
  • An eighth system, method and computer program product to generate an inventory array per person using region proposals and get/put analysis (e.g . Outputs of
  • a ninth system, method and computer program product to identify, track and update locations of misplaced items on shelves.
  • a fourteenth system, method and computer program product to perform checkout and collect payment from member customers.
  • An eighteenth system, method and computer program product to generate customer shopping analytics including one or more of location-based impressions, directional impressions, A/B analysis, customer recognition, group dynamics etc.
  • Described herein is a method for tracking puts and takes of inventory items by subjects in an area of real space, comprising:
  • processing the first data sets to specify bounding boxes which include images of hands of identified subjects in images in the sequences of images;
  • the first data sets can comprise for each identified subject sets of candidate joints having coordinates in real space.
  • This described method can include processing the first data sets to specify bounding boxes includes specifying bounding boxes based on locations of joints in the sets of candidate joints for each subject.
  • one or both of the first and the second image recognition engines can comprise convolutional neural networks.
  • This described method can include processing the classifications of bounding boxes using convolutional neural networks.
  • a computer program product and products are described which include a computer readable memory comprising a non-transitory data storage medium, and computer instructions stored in the memory executable by a computer to track puts and takes of inventory items by subjects in an area of real space by any of the herein described processes.
  • a system comprising a plurality of cameras producing a sequences of images including a hand of a subject; and a processing system coupled to the plurality of cameras, the processing system including a hand image recognition engine, receiving the sequence of images, to generate classifications of the hand in time sequence, and logic to process the classifications of the hand from the sequence of images to identify an action by the subject, wherein, the action is one of puts and takes of inventory items.
  • the system can include logic to identify locations of joints of the subject in the images in the sequences of images, and to identify bounding boxes in corresponding images that include the hands of the subject based on the identified joints.
  • a computer program listing appendix accompanies the specification, and includes portions of an example of a computer program to implement certain parts of the system provided in this application.
  • the appendix includes examples of heuristics to identify joints of subjects and inventory items.
  • the appendix presents computer program code to update a subject’s shopping cart data structure.
  • the appendix also includes a computer program routine to calculate learning rate during training of a convolutional neural network.
  • the appendix includes a computer program routine to store classification results of hands of subjects from a convolutional neural network in a data structure per hand per subject per image frame from each camera.

Abstract

L'invention concerne des systèmes et des techniques pour suivre des mises en place et des enlèvements d'articles d'inventaire par des sujets dans une zone d'un espace réel. Une pluralité de caméras ayant des champs de vision se chevauchant produisent des séquences respectives d'images de champs de vision correspondants dans l'espace réel. Dans un mode de réalisation, le système comprend des premiers processeurs d'image, comprenant des moteurs de reconnaissance d'image de sujet, recevant des séquences correspondantes d'images provenant de la pluralité des caméras. Les premiers processeurs d'image traitent des images pour identifier des sujets représentés dans les images dans les séquences d'images correspondantes. Le système comprend des seconds processeurs d'image, comprenant des moteurs de reconnaissance d'image d'arrière-plan, recevant des séquences correspondantes d'images provenant de la pluralité des caméras. Les seconds processeurs d'image masquent les sujets identifiés pour générer des images masquées. Ensuite, les seconds processeurs d'image traitent les images masquées pour identifier et classifier des changements d'arrière-plan représentés dans les images dans les séquences d'images correspondantes.
PCT/US2018/043937 2017-08-07 2018-07-26 Prédiction d'événements d'inventaire à l'aide d'une différenciation sémantique WO2019032306A1 (fr)

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CA3072058A CA3072058A1 (fr) 2017-08-07 2018-07-26 Prediction d'evenements d'inventaire a l'aide d'une differenciation semantique

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US15/847,796 2017-12-19
US15/847,796 US10055853B1 (en) 2017-08-07 2017-12-19 Subject identification and tracking using image recognition
US15/907,112 2018-02-27
US15/907,112 US10133933B1 (en) 2017-08-07 2018-02-27 Item put and take detection using image recognition
US15/945,466 2018-04-04
US15/945,473 US10474988B2 (en) 2017-08-07 2018-04-04 Predicting inventory events using foreground/background processing
US15/945,466 US10127438B1 (en) 2017-08-07 2018-04-04 Predicting inventory events using semantic diffing
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