WO2022176776A1 - 解析装置、解析システム、解析方法及びプログラム - Google Patents

解析装置、解析システム、解析方法及びプログラム Download PDF

Info

Publication number
WO2022176776A1
WO2022176776A1 PCT/JP2022/005401 JP2022005401W WO2022176776A1 WO 2022176776 A1 WO2022176776 A1 WO 2022176776A1 JP 2022005401 W JP2022005401 W JP 2022005401W WO 2022176776 A1 WO2022176776 A1 WO 2022176776A1
Authority
WO
WIPO (PCT)
Prior art keywords
product
products
display state
sales floor
processors
Prior art date
Legal status (The legal status 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 status listed.)
Ceased
Application number
PCT/JP2022/005401
Other languages
English (en)
French (fr)
Japanese (ja)
Inventor
叡一 松元
俊太 齋藤
大輔 西野
良博 山田
義文 丸山
優一 野々目
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Ito Yokado Co Ltd
Preferred Networks Inc
Original Assignee
Ito Yokado Co Ltd
Preferred Networks Inc
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.)
Filing date
Publication date
Application filed by Ito Yokado Co Ltd, Preferred Networks Inc filed Critical Ito Yokado Co Ltd
Priority to JP2023500803A priority Critical patent/JPWO2022176776A1/ja
Publication of WO2022176776A1 publication Critical patent/WO2022176776A1/ja
Priority to US18/449,925 priority patent/US20240037952A1/en
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration
    • G06T5/50—Image enhancement or restoration using two or more images, e.g. averaging or subtraction
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/70—Determining position or orientation of objects or cameras
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/20—Image preprocessing
    • G06V10/25—Determination of region of interest [ROI] or a volume of interest [VOI]
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/20—Image preprocessing
    • G06V10/255—Detecting or recognising potential candidate objects based on visual cues, e.g. shapes
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20212—Image combination
    • G06T2207/20221—Image fusion; Image merging
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30242—Counting objects in image

Definitions

  • the present disclosure relates to analysis devices, analysis systems, analysis methods, and programs.
  • the subject of the present disclosure is to provide a novel technique for analyzing the display state of merchandise.
  • one aspect of the present disclosure includes one or more memories and one or more processors, and the one or more processors estimate an arrangement area for product groups of the same type. and an analysis device for estimating the display state of the product group in the arrangement area.
  • FIG. 1 is a schematic diagram illustrating an analysis system according to one embodiment of the present disclosure
  • FIG. 2 is a diagram illustrating segmentation according to one embodiment of the present disclosure.
  • FIG. 3 is a schematic diagram illustrating an analysis process according to one embodiment of the disclosure.
  • FIG. 4 is a block diagram showing the functional configuration of an analysis device according to an embodiment of the present disclosure;
  • FIG. 5 is a diagram illustrating training data according to one embodiment of the present disclosure;
  • FIG. 6 is a diagram showing estimation results according to an embodiment of the present disclosure.
  • FIG. 7 is a flowchart illustrating analysis processing according to one embodiment of the present disclosure.
  • FIG. 8 is a block diagram showing the hardware configuration of an analysis device according to one embodiment of the present disclosure.
  • an analysis system that captures images of a store's sales floor and uses a machine learning model to estimate the display state of products based on the sales floor image.
  • FIG. 1 is a schematic diagram illustrating an analysis system according to one embodiment of the present disclosure
  • the analysis system 10 has an imaging device 20, a user terminal 30 and an analysis device 100.
  • the analysis device 100 analyzes the acquired sales floor image, and notifies the user terminal 30 of the display state of the imaged sales floor, an instruction of the display state, an instruction regarding the display state, and the like.
  • the display state may be, for example, the product name, the number of products, the product alignment degree, etc. for each product type displayed in the arrangement area of the sales floor.
  • the imaging device 20 may be, for example, a video camera installed in a store or the like, captures an image of the sales floor to be imaged, and transmits the sales floor image to the analysis device 100 .
  • the imaging device 20 is installed near a sales floor to be imaged and used to observe the sales floor.
  • the imaging device 20 may be fixed at a fixed location in the store, or may be a movable device provided on a robot or cart. Accordingly, various information can be obtained. Also, it is possible to reduce the number of imaging devices 20 to be installed. Also, a plurality of imaging devices 20 may be provided. As a result, it is possible to obtain an appropriate sales floor image even when there is a blind spot or the like.
  • the user terminal 30 may be, for example, an information processing device such as a personal computer, a tablet, or a smartphone provided in a store, etc., and analyzes information on the display state of various product groups in the sales floor estimated based on the sales floor image.
  • 100 or the analysis result of the analysis device 100 is acquired from a server or the like that has been saved.
  • the user terminal 30 may include software related to store operation and business improvement, such as various software for assisting salesclerks in performing product replenishment, replacement, pricing, and the like.
  • Software may be provided to view 100 analysis results.
  • the store clerk or the like replenishes, replaces, or prices the products in the sales floor based on the data analyzed by various software using the display state and POS (Point Of Sales) data acquired from the analysis device 100. good too.
  • the analysis device 100 may be, for example, a personal computer provided in a store, or an information processing device such as a server provided in a location different from the store, such as a headquarters that manages the store or on the cloud. From the acquired sales floor image, the arrangement area for each product type displayed in the sales floor is estimated, and the display state of the product group arranged in each arrangement area is estimated. Note that the analysis apparatus 100 may acquire the sales floor image acquired from the imaging device 20, or may acquire data obtained by subjecting the sales floor image to predetermined processing. In such a case, the sales floor image acquired by the imaging device 20 is output to a predetermined processing device, and the data processed by the processing device is output to the analysis device 100 . This makes it possible to facilitate the transmission of information on the sales floor image via the network and the subsequent processing in the analysis device 100 . When a plurality of imaging devices 20 are installed, one processing device may be provided for the plurality of imaging devices 20 .
  • the display state refers to the display state of the product group, and may be, for example, the product name, the number of products, the product alignment degree, etc. of the product group displayed in each arrangement area.
  • the analysis apparatus 100 uses a machine learning model such as a neural network to estimate the arrangement area for each product type from the sales floor image, and displays the same type of product group displayed in each arrangement area. state may be estimated.
  • the analysis apparatus 100 performs segmentation for each product type on the frame of the sales floor video as shown in FIG. 1, and acquires the segmented frame as shown in FIG.
  • the analysis apparatus 100 may perform real-time processing or batch processing of the sales floor images acquired from the imaging device 20 .
  • the analysis device 100 inputs a sales floor image into a machine learning model, divides the sales floor image into regions, and generates a product region map indicating the arrangement region for each product type, a sales floor map, and a sales floor map.
  • a product center heat map showing the center of each product placed in the store and a product orientation heat map showing the orientation of each product placed on the sales floor may be acquired.
  • the analysis device 100 can superimpose these data on the sales floor image to confirm the display state of each product.
  • the center does not strictly mean the center. Also, the calculation of the center can be performed by various methods.
  • FIG. 4 is a block diagram showing the functional configuration of the analysis device 100 according to one embodiment of the present disclosure.
  • the analysis device 100 has an area estimation unit 110 and a display state estimation unit 120 .
  • the region estimating unit 110 and the display state estimating unit 120 are installed in the analysis apparatus 100 and implemented by one or more processors executing one or more programs stored in one or more memories.
  • the region estimating unit 110 estimates the arrangement region of the group of products to be classified as the same (referred to as the group of products of the same type in this specification) from the sales floor image. Specifically, when the sales floor image is acquired from the imaging device 20, the area estimation unit 110 performs area division on the acquired sales floor image frame for each product type, and estimates a placement area for each product type. In a typical sales floor, various products are grouped and arranged on display shelves according to product type.
  • the area estimation unit 110 performs preprocessing such as, for example, removing moving objects such as people and shopping carts included in the sales floor image, and cutting out an attention area.
  • an arrangement area in which the product group of product type 1 is displayed, an arrangement area in which the product group of product type 2 is displayed, an arrangement region in which the product group of product type 3 is displayed, and a product type 4 Estimate the placement area where the product group is displayed.
  • the region estimating unit 110 may use a trained machine learning model to perform region segmentation based on the product type on the frame of the sales floor video, and estimate the placement region for each product type.
  • the machine learning model may be trained to, upon input of a frame of a sales floor video, divide the frame into regions and output a product region map indicating the placement region for each product type. For example, when the region estimating unit 110 acquires a product region map by inputting the frame of the sales floor video of the vegetable sales floor shown in FIG. A frame divided into areas for each type may be generated.
  • the machine learning model for area estimation may be realized as, for example, a neural network, and the frame of the sales floor image as shown in FIG. 1 and the arrangement area for each product type as shown in FIG. may be trained by supervised learning using pairs of frames annotated with information about .
  • the machine learning model may be an instance segmentation model such as Mask-RCNN (Regional Convolutional Neural Network). , and its corresponding segmentation mask.
  • the machine learning model may be a convolutional neural network and may be trained to segment by clustering feature vectors in the feature map. In other words, areas with similar feature vectors can be considered areas in which products of the same type are displayed.
  • a convolutional neural network may be trained by tuning a pretrained convolutional neural network on another large-scale image dataset such as Imagenet, or alternatively assigning temporary labels to product regions and It may be trained to predict label numbers.
  • region estimation section 110 may smooth frames in the time direction so that past prediction results do not change abruptly. In batch processing, not only past prediction results but also future prediction results can be used. On the other hand, when products are replenished or replaced, recognition of rapid change may be the correct answer. Therefore, the region estimating unit 110 may allow a sudden change without smoothing when the frame-to-frame difference of the sales floor video is greater than a predetermined threshold.
  • the display state estimation unit 120 estimates the display state of the product group in the placement area. For example, the display state estimation unit 120 may estimate one or more of the product name, the number of products, and the product alignment of the product group in the arrangement area. Specifically, the display state estimation unit 120 uses a trained machine learning model to determine the product name, product Estimate the display status such as the number of products and the degree of product arrangement. For example, when the display state estimating unit 120 detects that the number of products in an arrangement area with a sales floor to be imaged is small or the degree of arrangement of the products is low, it identifies the product name of the arrangement area and replenishes the product. Alternatively, the store clerk can be notified to arrange the display of the item.
  • the display state estimating unit 120 uses a trained machine learning model to estimate at least one of the product names, the number of products, and the degree of product arrangement as the display state. good.
  • the machine learning model is trained to receive a frame of store video and output the product name, center position and/or orientation of the product contained within the frame.
  • a product name may be indicated by product identification information such as a product number assigned in advance to the product name.
  • the center position of each product may be indicated by a symbol (for example, a circle) indicating the center of each product in the frame, or by a product center heat map such as that shown in FIG. may be shown.
  • each product may be indicated by a symbol (e.g., a straight line) indicating the orientation of each product in the frame, or by a product orientation heat map such as that shown in FIG. may
  • the machine learning model is trained to output at least one of the product name, product center, and product orientation of the product imaged in the frame when a frame of the sales floor video is input.
  • Such a machine learning model may be realized, for example, as a neural network, and annotated with information on frames of a sales floor video, product names for each product type in the frame, and the center and/or orientation of each product. It may be trained by supervised learning using pairs of frames as training data.
  • the display state estimating unit 120 uses a trained machine learning model to estimate the product name of a product group displayed in an arrangement area
  • the machine learning model can be used to estimate the product name of the product group captured in the input frame. It may specify product identification information such as the product number of the product. That is, the machine learning model is implemented as a neural network and trained by supervised learning using pairs of sales floor video frames and frames annotated with information related to product identification information for each product in the frames as training data. may be After acquiring the machine learning model trained in this way, the display state estimation unit 120 can use the machine learning model to estimate the product name of each product displayed in the frame of the sales floor video.
  • the input frame may be a frame segmented into regions by the region estimation unit 110, or may be a frame not segmented into regions.
  • the machine learning model may be a neural network that determines product feature values for each product type from the frames of the sales floor video.
  • the display state estimation unit 120 estimates the feature quantity of each product arranged in the frame using the machine learning model, the product name corresponding to the estimated feature quantity may be identified as the product.
  • the product may be determined as unknown.
  • the information may be used for estimation. For example, it is possible to narrow down the products to be placed in the analysis target sales floor from external information, and the product classification (such as vegetables, sweets, etc.) ) It is possible to acquire a machine learning model for each type and improve the estimation accuracy.
  • the machine learning model can, for example, estimate the number of products captured in the frame from the input frame. , such as the center of the item. That is, the machine learning model is realized as a neural network and trained by supervised learning using pairs of frames of sales floor images and frames with annotations indicating the center of each product in the frame as training data. good.
  • FIG. 5 is an example of an annotated frame with the center of each product. In the annotated frame shown, each item is annotated with a circle indicating the center of the packaged item.
  • the display state estimation unit 120 uses the machine learning model to estimate the center of each product displayed in the frame of the sales floor image, and the region estimation unit 110 It is possible to estimate the number of products displayed in each placement area based on the estimated number of center points in each placement area by referring to the frame divided into areas by .
  • the display state estimating unit 120 uses both a machine learning model for identifying the product name and a machine learning model for estimating the center point of the product to determine the placement of each region-divided frame as shown in FIG. It is possible to generate information indicating the product name of the product group arranged in the area and the center of each product.
  • the display state estimation unit 120 can estimate the product name and the number of products for each product type by counting the number of points (preferably center points) included in each placement area based on the frame. In addition, the number of products is estimated based on the part where the products are displayed in the placement area (e.g. shelves, the bottom of the product placement part of the fixture, etc.) is exposed, that is, the part where the products are missing. good too.
  • the display state determination unit determines that the placement region needs to be supplemented with products, and the display state instruction unit instructs the clerk For example, it may be instructed to replenish the arrangement area with the product.
  • the estimation of the number of products is not limited to this, and a machine learning model that detects by a bounding box indicating the position of each product within a frame may be used instead of points indicating products.
  • the display state estimation unit 120 may estimate the number of products by counting the number of bounding boxes included in each placement area. Alternatively, it may be estimated using product density. For example, the product point heat map may be regarded as the product density, and the number of products may be estimated by integrating the product center heat map for each placement region. Alternatively, the display state estimation unit 120 may estimate the number of products in each placement region of the frame using a machine learning model trained to regress the number of products from the feature amount of the placement region. Estimating the number of items by regression of item density and number of items described above may also predict the number of hidden items that are not captured in the frame if the machine learning model is properly trained.
  • the machine learning model is, for example, It may specify the orientation of the product. That is, the machine learning model is implemented as, for example, a neural network, and is trained by supervised learning using pairs of sales floor video frames and frames with annotations regarding the orientation of each product in the frames as training data. good too.
  • FIG. 5 is an example of an annotated frame with orientation information of each product. In the illustrated annotated frame, each item is annotated with a straight line indicating the orientation of the packaged items.
  • the display state estimation unit 120 estimates the orientation of each product displayed in the frame of the sales floor image, and refers to the frame segmented into areas by the area estimation unit 110. Then, it is possible to estimate the degree of alignment of the products displayed in each placement area based on how well the estimated orientations in each placement area are aligned. For example, the display state estimating unit 120 first predicts the center of each product in the placement area by using both a machine learning model for estimating the product center and a machine learning model for estimating product orientation. Orientation may be predicted to determine how much variation there is in the orientation of each item contained within the placement area.
  • the display state estimation unit 120 may define the maximum value of the difference in the product orientations of the product group within the arrangement area as the product alignment degree.
  • the display state determination unit determines that the placement region needs to be aligned, and the display state instruction unit issues instructions via the user terminal or the like. , the store clerk or the like may be instructed to arrange the commodities displayed in the arrangement area.
  • the estimation of the degree of product alignment is not limited to this, and feature amounts may be used.
  • the display state estimation unit 120 may first predict the center of each product in the placement area, and determine how much the feature vector of the feature map for each product center varies within the placement area. This is because the fact that the feature vectors of the respective products are scattered means that there are variations in the local appearance, and it is considered that the direction of the products is not uniform.
  • the variations may be estimated for a plurality of products, for example, two products placed in close proximity, and aggregated over the entire placement area.
  • the display state estimating unit 120 searches for k products in the neighborhood of each product in the arrangement area, and determines the matching degree of orientation between the product in question and each neighboring product by using the inner product of feature vectors. may be evaluated. Then, the display state estimating unit 120 may calculate the degree of matching for all pairs of neighboring products in the arrangement area and average them, thereby determining the degree of product arrangement for the arrangement area. Alternatively, the display state estimation unit 120 may use a machine learning model trained to calculate the feature amount of the placement area, and use the feature amount calculated for the placement area in the frame as the product alignment degree. good.
  • FIG. 7 is a flowchart illustrating analysis processing according to one embodiment of the present disclosure.
  • the analysis device 100 acquires a sales floor image. Specifically, the analysis device 100 acquires the sales floor image from the imaging device 20 installed in the sales floor.
  • the analysis apparatus 100 may execute the following steps on the acquired sales floor image in real time, or temporarily store the acquired sales floor image and store the stored sales floor image at an appropriate timing. The following steps may be performed on the video.
  • data obtained by processing the sales floor image (that is, data based on the sales floor image) may be acquired.
  • the analysis device 100 estimates the arrangement area of the product group of the same type from the sales floor image. For example, the analysis device 100 divides the frame of the sales floor video into regions, and uses a machine learning model such as a neural network that has been pre-trained to estimate the placement region for each product type displayed in the sales floor. The arrangement area of the product group displayed in the sales floor may be estimated from the frame.
  • a machine learning model such as a neural network that has been pre-trained to estimate the placement region for each product type displayed in the sales floor.
  • the arrangement area of the product group displayed in the sales floor may be estimated from the frame.
  • step S103 the analysis device 100 estimates the display state of the product group in the placement area.
  • the analysis device 100 uses a neural network or the like trained in advance to estimate one or more of the product name, the number of products, and the degree of product alignment in the arrangement area for each product type from the frame of the sales floor video.
  • a machine learning model is used to estimate one or more of the product name, the number of products, and the degree of arrangement of products for each type of product displayed on the sales floor from the frame of the sales floor video.
  • the analysis device 100 uses a machine learning model such as a neural network trained to estimate the center and/or orientation of each product in each placement region segmented from the frame, and each product in the placement region
  • the number of products may be calculated based on the number at the center of the area, and the degree of product alignment may be determined based on the degree of matching of the orientation of each product within the arrangement area.
  • step S104 the analysis device 100 determines whether the display state satisfies a predetermined condition. Specifically, the analysis device 100 determines whether the display state requires maintenance by a store clerk or the like. For example, the analysis device 100 may detect whether there is an arrangement area in which the estimated number of products is less than a predetermined value. Alternatively, analysis device 100 may detect whether the estimated product alignment is less than a predetermined value. Alternatively, it may be detected whether the area of the product area is less than the set value. In this way, when an arrangement region which is less than the predetermined value and satisfies the prescribed condition is detected (S104: YES), in step S105, the analysis device 100 detects the detected arrangement region via the user terminal or the like. Notify the store clerk, etc. to replenish or arrange the merchandise. On the other hand, if no placement area that satisfies the predetermined condition is detected (S104: NO), the analysis device 100 returns to step S101 and repeats the steps described above.
  • the analysis device 100 may store or output to an external storage device or the like a log regarding the satisfaction of a predetermined condition.
  • the log can be used for store management work, business improvement, and the like. Further, for example, such a log may be stored in association with a sales floor video determined to satisfy a predetermined condition. As a result, it can be easily used for store management work, business improvement, and the like.
  • Part or all of the analysis device 100 in the above-described embodiment may be configured by hardware, or information on software (program) executed by a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), or the like. processing.
  • software information processing software that realizes at least part of the functions of each device in the above-described embodiments can be transferred to a flexible disk, a CD-ROM (Compact Disc-Read Only Memory), or a USB (Universal Serial Bus) memory or other non-temporary storage medium (non-temporary computer-readable medium) and read by a computer to execute software information processing.
  • the software may be downloaded via a communication network.
  • information processing may be performed by hardware by implementing software in a circuit such as an ASIC (Application Specific Integrated Circuit) or an FPGA (Field Programmable Gate Array).
  • the type of storage medium that stores the software is not limited.
  • the storage medium is not limited to a detachable one such as a magnetic disk or an optical disk, and may be a fixed storage medium such as a hard disk or memory. Also, the storage medium may be provided inside the computer, or may be provided outside the computer.
  • FIG. 8 is a block diagram showing an example of the hardware configuration of the analysis device 100 in the embodiment described above.
  • the analysis device 100 includes, for example, a processor 71 , a main storage device 72 (memory), an auxiliary storage device 73 (memory), a network interface 74 , and a device interface 75 . It may also be implemented as a connected computer 7 .
  • the computer 7 in FIG. 8 has one of each component, it may have a plurality of the same components.
  • the software is installed in a plurality of computers, and each of the plurality of computers executes the same or different part of the processing of the software. good too. In this case, it may be in the form of distributed computing in which each computer communicates via the network interface 74 or the like to execute processing.
  • the analysis apparatus 100 in the above-described embodiment may be configured as a system in which functions are realized by one or more computers executing instructions stored in one or more storage devices. Further, the information transmitted from the terminal may be processed by one or more computers provided on the cloud, and the processing result may be transmitted to the terminal.
  • Various operations of the analysis device 100 in the above-described embodiment may be executed in parallel using one or more processors or using multiple computers via a network. Also, various operations may be distributed to a plurality of operation cores in the processor and executed in parallel. Also, part or all of the processing, means, etc. of the present disclosure may be executed by at least one of a processor and a storage device provided on a cloud capable of communicating with the computer 7 via a network. Thus, the analysis device 100 in the above-described embodiments may be in the form of parallel computing by one or more computers.
  • the processor 71 may be an electronic circuit (processing circuit, processing circuit, CPU, GPU, FPGA, ASIC, etc.) including a computer control device and arithmetic device. Also, the processor 71 may be a semiconductor device or the like including a dedicated processing circuit. The processor 71 is not limited to an electronic circuit using electronic logic elements, and may be realized by an optical circuit using optical logic elements. Also, the processor 71 may include arithmetic functions based on quantum computing.
  • the processor 71 can perform arithmetic processing based on the data and software (programs) input from each device, etc. of the internal configuration of the computer 7, and output the arithmetic result and control signal to each device, etc.
  • the processor 71 may control each component of the computer 7 by executing the OS (Operating System) of the computer 7, applications, and the like.
  • the analysis device 100 in the above-described embodiment may be realized by one or more processors 71.
  • the processor 71 may refer to one or more electronic circuits arranged on one chip, or one or more electronic circuits arranged on two or more chips or two or more devices. You can point When multiple electronic circuits are used, each electronic circuit may communicate by wire or wirelessly.
  • the main storage device 72 is a storage device that stores commands executed by the processor 71 and various types of data.
  • the auxiliary storage device 73 is a storage device other than the main storage device 72 .
  • These storage devices mean any electronic components capable of storing electronic information, and may be semiconductor memories.
  • the semiconductor memory may be either volatile memory or non-volatile memory.
  • a storage device for storing various data in the analysis device 100 in the above-described embodiment may be implemented by the main storage device 72 or the auxiliary storage device 73, or may be implemented by a built-in memory built into the processor 71.
  • the storage unit 72 in the above-described embodiment may be realized by the main storage device 72 or the auxiliary storage device 73.
  • a plurality of processors may be connected (coupled) to one storage device (memory), or a single processor may be connected.
  • a plurality of storage devices (memories) may be connected (coupled) to one processor.
  • the analysis device 100 in the above-described embodiment is composed of at least one storage device (memory) and a plurality of processors connected (coupled) to this at least one storage device (memory)
  • at least One processor may include a configuration that is connected (coupled) to at least one storage device (memory). Also, this configuration may be realized by storage devices (memory) and processors included in a plurality of computers.
  • a configuration in which a storage device (memory) is integrated with a processor for example, a cache memory including an L1 cache and an L2 cache
  • a cache memory for example, a cache memory including an L1 cache and an L2 cache
  • the network interface 74 is an interface for connecting to the communication network 8 wirelessly or by wire. As for the network interface 74, an appropriate interface such as one conforming to existing communication standards may be used. The network interface 74 may exchange information with the external device 9A connected via the communication network 8 .
  • the communication network 8 may be any one of WAN (Wide Area Network), LAN (Local Area Network), PAN (Personal Area Network), etc., or a combination of them. It is sufficient if information can be exchanged between them. Examples of WANs include the Internet, examples of LANs include IEEE 802.11 and Ethernet (registered trademark), and examples of PANs include Bluetooth (registered trademark) and NFC (Near Field Communication).
  • the device interface 75 is an interface such as USB that directly connects with the external device 9B.
  • the external device 9A is a device connected to the computer 7 via a network.
  • the external device 9B is a device that is directly connected to the computer 7. FIG.
  • the external device 9A or the external device 9B may be an input device.
  • the input device is, for example, a device such as a camera, microphone, motion capture, various sensors, keyboard, mouse, or touch panel, and provides the computer 7 with acquired information.
  • a device such as a personal computer, a tablet terminal, or a smartphone including an input unit, a memory, and a processor may be used.
  • the external device 9A or the external device 9B may be an output device as an example.
  • the output device may be, for example, a display device such as LCD (Liquid Crystal Display), CRT (Cathode Ray Tube), PDP (Plasma Display Panel), or organic EL (Electro Luminescence) panel, and output audio etc. It may be a speaker or the like that Alternatively, a device such as a personal computer, a tablet terminal, or a smartphone including an output unit, a memory, and a processor may be used.
  • the external device 9A or the external device 9B may be a storage device (memory).
  • the external device 9A may be a network storage or the like, and the external device 9B may be a storage such as an HDD.
  • the external device 9A or the external device 9B may be a device having the functions of some of the components of each device (server 100 or terminal 200) in the above-described embodiments. That is, the computer 7 may transmit or receive part or all of the processing results of the external device 9A or the external device 9B.
  • the expression "at least one (one) of a, b and c" or “at least one (one) of a, b or c" includes any of a, b, c, a-b, ac, b-c, or a-b-c. Also, multiple instances of any element may be included, such as a-a, a-b-b, a-a-b-b-c-c, and so on. It also includes the addition of other elements than the listed elements (a, b and c), such as having d such as a-b-c-d.
  • connection and “coupled” when used, they refer to direct connection/coupling, indirect connection/coupling , electrically connected/coupled, communicatively connected/coupled, operatively connected/coupled, physically connected/coupled, etc. intended as a term.
  • the term should be interpreted appropriately according to the context in which the term is used, but any form of connection/bonding that is not intentionally or naturally excluded is not included in the term. should be interpreted restrictively.
  • the physical structure of element A is such that it is capable of performing operation B configuration, including that a permanent or temporary setting/configuration of element A is configured/set to actually perform action B good.
  • element A is a general-purpose processor
  • the processor has a hardware configuration capable of executing operation B, and operation B is performed by setting a permanent or temporary program (instruction). It just needs to be configured to actually run.
  • the element A is a dedicated processor or a dedicated arithmetic circuit, etc., regardless of whether or not control instructions and data are actually attached, the circuit structure of the processor actually executes the operation B. It just needs to be implemented.
  • finding a global optimum finding an approximation of a global optimum, finding a local optimum, and finding a local optimum It includes approximations of values and should be interpreted accordingly depending on the context in which the term is used. It also includes stochastically or heuristically approximating these optimum values.
  • each piece of hardware may work together to perform the predetermined processing, or a part of the hardware may perform the predetermined processing. You may do all of Also, some hardware may perform a part of the predetermined processing, and another hardware may perform the rest of the predetermined processing.
  • the hardware that performs the first process and the hardware that performs the second process may be the same or different. In other words, the hardware that performs the first process and the hardware that performs the second process may be included in the one or more pieces of hardware.
  • hardware may include an electronic circuit or a device including an electronic circuit.
  • each storage device (memory) among the plurality of storage devices (memories) stores only part of the data. may be stored, or the entirety of the data may be stored.

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Multimedia (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Image Analysis (AREA)
PCT/JP2022/005401 2021-02-17 2022-02-10 解析装置、解析システム、解析方法及びプログラム Ceased WO2022176776A1 (ja)

Priority Applications (2)

Application Number Priority Date Filing Date Title
JP2023500803A JPWO2022176776A1 (https=) 2021-02-17 2022-02-10
US18/449,925 US20240037952A1 (en) 2021-02-17 2023-08-15 Analysis device, analysis system, analysis method, and computer-readable medium

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
JP2021023664 2021-02-17
JP2021-023664 2021-02-17

Related Child Applications (1)

Application Number Title Priority Date Filing Date
US18/449,925 Continuation US20240037952A1 (en) 2021-02-17 2023-08-15 Analysis device, analysis system, analysis method, and computer-readable medium

Publications (1)

Publication Number Publication Date
WO2022176776A1 true WO2022176776A1 (ja) 2022-08-25

Family

ID=82931696

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/JP2022/005401 Ceased WO2022176776A1 (ja) 2021-02-17 2022-02-10 解析装置、解析システム、解析方法及びプログラム

Country Status (3)

Country Link
US (1) US20240037952A1 (https=)
JP (1) JPWO2022176776A1 (https=)
WO (1) WO2022176776A1 (https=)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2024195090A1 (ja) * 2023-03-23 2024-09-26 日本電気株式会社 店舗業務支援装置、店舗業務支援方法、及びコンピュータ可読媒体

Families Citing this family (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP4128040B1 (en) * 2020-04-24 2026-01-21 Shanghai United Imaging Healthcare Co., Ltd. Systems and methods for object recognition
WO2021249439A1 (en) 2020-06-09 2021-12-16 Shanghai United Imaging Healthcare Co., Ltd. Systems and methods for image processing

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP6008339B1 (ja) * 2015-04-28 2016-10-19 パナソニックＩｐマネジメント株式会社 商品モニタリング装置、商品モニタリングシステムおよび商品モニタリング方法
JP2017007861A (ja) * 2015-06-23 2017-01-12 東芝テック株式会社 画像処理装置
JP6695539B1 (ja) * 2019-08-16 2020-05-20 株式会社イーアイアイ 物品選別装置、物品選別システムおよび物品選別方法
JP2021000687A (ja) * 2019-06-21 2021-01-07 キヤノンマーケティングジャパン株式会社 ロボットシステム、その制御方法とプログラム

Family Cites Families (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2015210723A (ja) * 2014-04-28 2015-11-24 株式会社日立システムズ 販売支援システム及び販売支援方法
JP6751882B2 (ja) * 2016-03-31 2020-09-09 パナソニックＩｐマネジメント株式会社 商品モニタリング装置、商品モニタリングシステムおよび商品モニタリング方法
JP7274292B2 (ja) * 2019-01-11 2023-05-16 東芝テック株式会社 支援システム、支援装置及び情報処理プログラム
US11562500B2 (en) * 2019-07-24 2023-01-24 Squadle, Inc. Status monitoring using machine learning and machine vision
US11715278B2 (en) * 2020-09-11 2023-08-01 Sensormatic Electronics, LLC Real time tracking of shelf activity supporting dynamic shelf size, configuration and item containment
US20220092664A1 (en) * 2020-09-24 2022-03-24 Grabango Co. Optimization of product presentation

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP6008339B1 (ja) * 2015-04-28 2016-10-19 パナソニックＩｐマネジメント株式会社 商品モニタリング装置、商品モニタリングシステムおよび商品モニタリング方法
JP2017007861A (ja) * 2015-06-23 2017-01-12 東芝テック株式会社 画像処理装置
JP2021000687A (ja) * 2019-06-21 2021-01-07 キヤノンマーケティングジャパン株式会社 ロボットシステム、その制御方法とプログラム
JP6695539B1 (ja) * 2019-08-16 2020-05-20 株式会社イーアイアイ 物品選別装置、物品選別システムおよび物品選別方法

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2024195090A1 (ja) * 2023-03-23 2024-09-26 日本電気株式会社 店舗業務支援装置、店舗業務支援方法、及びコンピュータ可読媒体

Also Published As

Publication number Publication date
US20240037952A1 (en) 2024-02-01
JPWO2022176776A1 (https=) 2022-08-25

Similar Documents

Publication Publication Date Title
US20240144340A1 (en) Remote SKU On-Boarding of Products for Subsequent Video Identification and Sale
WO2022176776A1 (ja) 解析装置、解析システム、解析方法及びプログラム
JP7208974B2 (ja) 画像認識を用いた商品を置くこと及び取ることの検出
CN114066900A (zh) 图像分割方法、装置、电子设备和存储介质
US20190138676A1 (en) Methods and systems for automatically creating statistically accurate ergonomics data
CN106776619A (zh) 用于确定目标对象的属性信息的方法和装置
US20180293753A1 (en) Estimating multi-person poses using greedy part assignment
JP2024040297A (ja) 物品推定装置、物品推定方法、及びプログラム
CN109886169B (zh) 应用于无人货柜的物品识别方法、装置、设备及存储介质
US11449718B2 (en) Methods and systems of real time movement classification using a motion capture suit
US20230267486A1 (en) Computer-readable recording medium, information processing method, and information processing apparatus
CN101625722A (zh) 载物识别图像的分类方法
JP2009251779A (ja) 販売数予測システム、販売数予測方法および販売数予測プログラム
JP6806159B2 (ja) 動線分類装置、動線分類方法およびプログラム
CN114782812A (zh) 一种智能秤干扰识别方法、装置、系统及存储介质
US20220284600A1 (en) User identification in store environments
CN108875822A (zh) 数据获取方法、装置、电子设备及计算机可读存储介质
US20240153274A1 (en) Artificial intelligence enabled distance event detection using image analysis
CN112154488B (zh) 信息处理装置、控制方法和程序
EP4177848A1 (en) Method and system for determining contextual object position
WO2022176774A1 (ja) 解析装置、解析システム、解析方法及びプログラム
CN111310618A (zh) 物品识别方法、装置、电子设备及可读存储介质
TWI801251B (zh) 智慧補貨監控系統及其方法
CN117121029A (zh) 作业分析装置
US20240193671A1 (en) Non-transitory computer-readable recording medium, information processing method, and information processing device

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 22756096

Country of ref document: EP

Kind code of ref document: A1

WWE Wipo information: entry into national phase

Ref document number: 2023500803

Country of ref document: JP

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 22756096

Country of ref document: EP

Kind code of ref document: A1