WO2019203921A1 - Système de détection et de reconnaissance d'objets en temps réel à l'aide de caractéristiques d'image et de taille - Google Patents

Système de détection et de reconnaissance d'objets en temps réel à l'aide de caractéristiques d'image et de taille Download PDF

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Publication number
WO2019203921A1
WO2019203921A1 PCT/US2019/018119 US2019018119W WO2019203921A1 WO 2019203921 A1 WO2019203921 A1 WO 2019203921A1 US 2019018119 W US2019018119 W US 2019018119W WO 2019203921 A1 WO2019203921 A1 WO 2019203921A1
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Prior art keywords
cnn
confidence score
target
modified
input image
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PCT/US2019/018119
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English (en)
Inventor
Yang Chen
Deepak Khosla
Ryan M. UHLENBROCK
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Hrl Laboratories, Llc
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Priority to CN201980016839.5A priority Critical patent/CN111801689A/zh
Priority to EP19789101.3A priority patent/EP3782075A4/fr
Publication of WO2019203921A1 publication Critical patent/WO2019203921A1/fr

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    • 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
    • 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
    • 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
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G06F18/243—Classification techniques relating to the number of classes
    • G06F18/24323—Tree-organised classifiers
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/25—Fusion techniques
    • G06F18/254—Fusion techniques of classification results, e.g. of results related to same input data
    • 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
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/047—Probabilistic or stochastic networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/10—Image acquisition
    • G06V10/12—Details of acquisition arrangements; Constructional details thereof
    • G06V10/14—Optical characteristics of the device performing the acquisition or on the illumination arrangements
    • G06V10/143—Sensing or illuminating at different wavelengths
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/10—Image acquisition
    • G06V10/12—Details of acquisition arrangements; Constructional details thereof
    • G06V10/14—Optical characteristics of the device performing the acquisition or on the illumination arrangements
    • G06V10/145—Illumination specially adapted for pattern recognition, e.g. using gratings
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/20—Image preprocessing
    • G06V10/25—Determination of region of interest [ROI] or a volume of interest [VOI]
    • G—PHYSICS
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    • 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
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    • G06V10/40—Extraction of image or video features
    • G06V10/50—Extraction of image or video features by performing operations within image blocks; by using histograms, e.g. histogram of oriented gradients [HoG]; by summing image-intensity values; Projection analysis
    • G06V10/507—Summing image-intensity values; Histogram projection analysis
    • 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
    • G06V20/56—Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle
    • G06V20/58—Recognition of moving objects or obstacles, e.g. vehicles or pedestrians; Recognition of traffic objects, e.g. traffic signs, traffic lights or roads
    • G—PHYSICS
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    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
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    • 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

Definitions

  • the present invention relates to an object detection system and, more specifically, to an object detection and recognition system using both image and size features.
  • Object detection and recognition systems are often employed in autonomous vehicles and reconnaissance systems to quickly and automatically detect and recognize objects within a field-of-view.
  • Traditional object detection and recognition systems attempt to identify the object based on image features of the object. While functional, such systems are limited in their inability to validate the recognition based on size features.
  • Other attempts to determine size have been made using estimates of a ground- plane. See, for example, Dragon R., Van Gool L., "Ground plane estimation using a hidden Markov model", published at the 27th IEEE conference on computer vision and pattern recognition - CVPR 2014, pp. 4026-4033, June 23- 28, 2014, Columbus, Ohio, ETSA, the entirety of which is incorporated herein by reference.
  • this approach can often fail when there are occlusions, shadows or other issues not providing a clear view of open space (e.g., vehicle moving in a wooded area).
  • This disclosure provides an object recognition system.
  • the system includes one or more processors and a memory.
  • the memory includes executable instructions encoded thereon, such that upon execution of the instructions, the one or more processors perform the operations as described herein.
  • ICF integral channel features
  • the system extracts a candidate target region (having an associated original confidence score representing a candidate object) from an input image of a scene surrounding a platform.
  • a modified confidence score is generated based on a location and height of detection of the candidate object.
  • the candidate target regions are classified based on the modified confidence score using a trained convolutional neural network (CNN) classifier, resulting in classified objects.
  • CNN convolutional neural network
  • the classified objects are tracked using a multi-target tracker for final
  • classification of each classified object as a target or non-target. If the classified object is a target, a device can be controlled based on the target.
  • the ICF detector computes channel feature vectors for
  • an ICF classifier is applied at multiple image scales and across the entire image frame.
  • the CNN classifier is implemented as interacting
  • software modules comprising a CNN interface and a CNN server, wherein the CNN interface displays results received from the CNN server.
  • the trained CNN is used for both electro-optical (EO) and infrared (IR) image classification.
  • the input image is divided into a plurality of horizontal bands and ground truth objects are put into a same number of groups based on whether a location of the ground truth objects in the input image is in the band, with the objects in each group being used to estimate the mean and standard deviation of object height distribution in the input image.
  • generating the modified confidence score uses a weighted Gaussian according to the following equation:
  • modified confidence score original confidence score * wf , wherein h denotes a height of the candidate object in the input image, m and s denote mean and standard deviation, respectively, of object height distribution in the input image and bin, exp(.) denotes an exponential function, N is a multiplier and * denotes multiplication.
  • generating the modified confidence score uses a
  • modified confidence score original confidence score * wf , wherein h denotes a height of the candidate object in the input image, m and s denote mean and standard deviation, respectively, of object height distribution in the input image and bin, N is a multiplier and * denotes multiplication.
  • the one or more processors perform operations of
  • CNN-2 modified convolution network
  • the present invention also includes a computer program product and a computer implemented method.
  • the computer program product includes computer-readable instructions stored on a non-transitory computer-readable medium that are executable by a computer having one or more processors, such that upon execution of the instructions, the one or more processors perform the operations listed herein.
  • the computer implemented method includes an act of causing a computer to execute such instructions and perform the resulting operations.
  • FIG. 1 is a block diagram depicting the components of a system according to various embodiments of the present invention.
  • FIG. 2 is an illustration of a computer program product embodying an aspect of the present invention
  • FIG. 3 is a system block diagram according to various embodiments of the present invention.
  • FIG. 4 A is a system block diagram according to various embodiments of the present invention.
  • FIG. 4B is a system block diagram depicting a modified convolution network classifier according to various embodiments of the present invention.
  • FIG. 5 is an image illustrating an image frame as being divided into N
  • FIG. 6 A is an illustration depicting example height distribution of objects in
  • FIG. 6B is an illustration depicting example height distribution of objects in 88 training sequences for a front-facing sensor
  • FIG. 7 is a graph illustrating a comparison of detection scores weighted and unweighted for 30 test sequences
  • FIG. 8 is a graph illustrating post-CNN (Stage 2) receiver operating
  • FIG. 9 A is an illustration depicting example height distribution of ground truth objects (e.g., a person, a dismount) in 88 training sequences for the side- facing sensors;
  • ground truth objects e.g., a person, a dismount
  • FIG. 9B is an illustration depicting example height distribution of ground truth MAN objects in 88 training sequences for the front facing sensors
  • FIG. 10 is a graph illustrating the result comparing detection score weighted versus unweights ROCs for 30 EO test sequences in both the Pre-CNN (Stage 1 + size filtering) and Post-CNN (Stage 2) results; and [00040]
  • FIG. 11 is a block diagram depicting control of a device according to various embodiments.
  • the present invention relates to an object detection system and, more
  • the first is a system for object detection and recognition.
  • the system is typically in the form of a computer system operating software or in the form of a“hard- coded” instruction set. This system may be incorporated into a wide variety of devices that provide different functionalities.
  • the second principal aspect is a method, typically in the form of software, operated using a data processing system (computer).
  • the third principal aspect is a computer program product.
  • the computer program product generally represents computer-readable instructions stored on a non-transitory computer-readable medium such as an optical storage device, e.g., a compact disc (CD) or digital versatile disc (DVD), or a magnetic storage device such as a floppy disk or magnetic tape.
  • Other, non-limiting examples of computer-readable media include hard disks, read-only memory (ROM), and flash-type memories.
  • FIG. 1 A block diagram depicting an example of a system (i.e., computer system
  • the computer system 100 is configured to perform calculations, processes, operations, and/or functions associated with a program or algorithm.
  • certain processes and steps discussed herein are realized as a series of instructions (e.g., software program) that reside within computer readable memory units and are executed by one or more processors of the computer system 100. When executed, the instructions cause the computer system 100 to perform specific actions and exhibit specific behavior, such as described herein.
  • the computer system 100 may include an address/data bus 102 that is
  • processors configured to communicate information.
  • one or more data processing units such as a processor 104 (or processors) are coupled with the address/data bus 102.
  • the processor 104 is configured to process information and instructions.
  • the processor 104 is a microprocessor.
  • the processor 104 may be a different type of processor such as a parallel processor, application-specific integrated circuit (ASIC), programmable logic array (PLA), complex programmable logic device (CPLD), or a field
  • FPGA programmable gate array
  • the computer system 100 is configured to utilize one or more data storage units.
  • the computer system 100 may include a volatile memory unit 106 (e.g., random access memory (“RAM”), static RAM, dynamic RAM, etc.) coupled with the address/data bus 102, wherein a volatile memory unit 106 is configured to store information and instructions for the processor 104.
  • the computer system 100 further may include a non-volatile memory unit 108 (e.g., read-only memory (“ROM”), programmable ROM (“PROM”), erasable programmable ROM
  • the computer system 100 may execute instructions retrieved from an online data storage unit such as in“Cloud” computing.
  • the computer system 100 also may include one or more interfaces, such as an interface 110, coupled with the address/data bus 102. The one or more interfaces are configured to enable the computer system 100 to interface with other electronic devices and computer systems.
  • the communication interfaces implemented by the one or more interfaces may include wireline (e.g., serial cables, modems, network adaptors, etc.) and/or wireless (e.g., wireless modems, wireless network adaptors, etc.) communication technology.
  • wireline e.g., serial cables, modems, network adaptors, etc.
  • wireless e.g., wireless modems, wireless network adaptors, etc.
  • the computer system 100 may include an input device 112
  • the input device 112 is coupled with the address/data bus 102, wherein the input device 112 is configured to communicate information and command selections to the processor 100.
  • the input device 112 is an alphanumeric input device, such as a keyboard, that may include alphanumeric and/or function keys.
  • the input device 112 may be an input device other than an alphanumeric input device.
  • the computer system 100 may include a cursor control device 114 coupled with the address/data bus 102, wherein the cursor control device 114 is configured to communicate user input information and/or command selections to the processor 100.
  • the cursor control device 114 is implemented using a device such as a mouse, a track-ball, a track pad, an optical tracking device, or a touch screen.
  • the cursor control device 114 is directed and/or activated via input from the input device 112, such as in response to the use of special keys and key sequence commands associated with the input device 112.
  • the cursor control device 114 is configured to be directed or guided by voice commands.
  • the computer system 100 further may include one or more
  • a storage device 116 coupled with the address/data bus 102.
  • the storage device 116 is configured to store information and/or computer executable instructions.
  • the storage device 116 is a storage device such as a magnetic or optical disk drive (e.g., hard disk drive (“HDD”), floppy diskette, compact disk read only memory (“CD-ROM”), digital versatile disk (“DVD”)).
  • a display device 118 is coupled with the address/data bus 102, wherein the display device 118 is configured to display video and/or graphics.
  • the display device 118 may include a cathode ray tube (“CRT”), liquid crystal display (“LCD”), field emission display (“FED”), plasma display, or any other display device suitable for displaying video and/or graphic images and alphanumeric characters recognizable to a user.
  • CTR cathode ray tube
  • LCD liquid crystal display
  • FED field emission display
  • plasma display or any other display device suitable for displaying video and/or graphic images and alphanumeric characters recognizable to a user.
  • the computer system 100 presented herein is an example computing
  • the non-limiting example of the computer system 100 is not strictly limited to being a computer system.
  • the computer system 100 represents a type of data processing analysis that may be used in accordance with various aspects described herein.
  • other computing systems may also be implemented.
  • the spirit and scope of the present technology is not limited to any single data processing environment.
  • one or more operations of various aspects of the present technology are controlled or implemented using computer-executable instructions, such as program modules, being executed by a computer.
  • program modules include routines, programs, objects, components and/or data structures that are configured to perform particular tasks or implement particular abstract data types.
  • an aspect provides that one or more aspects of the present technology are implemented by utilizing one or more distributed computing environments, such as where tasks are performed by remote processing devices that are linked through a communications network, or such as where various program modules are located in both local and remote computer- storage media including memory- storage devices.
  • FIG. 2 An illustrative diagram of a computer program product (i.e., storage device) embodying the present invention is depicted in FIG. 2.
  • the computer program product is depicted as floppy disk 200 or an optical disk 202 such as a CD or DVD.
  • the computer program product generally represents computer-readable instructions stored on any compatible non-transitory computer-readable medium.
  • the term“instructions” as used with respect to this invention generally indicates a set of operations to be performed on a computer, and may represent pieces of a whole program or individual, separable, software modules.
  • Non-limiting examples of“instruction” include computer program code (source or object code) and“hard-coded” electronics (i.e. computer operations coded into a computer chip).
  • The“instruction” is stored on any non-transitory computer-readable medium, such as in the memory of a computer or on a floppy disk, a CD-ROM, and a flash drive. In either event, the instructions are encoded on a non-transitory computer-readable medium.
  • the present disclosure provides an object detection and recognition system that uses both image and size/location features.
  • the system extends upon the disclosure of U.S. Application No. 15/883,822, which only used image features.
  • the system of the present disclosure is operable for: 1) learning from image and location data to accurately detect and recognize targets; 2) confidence tuning of detection results based on location data; and 3) combining all of the above into integrated system as a single pipeline.
  • this disclosure provides a marked technological improvement to the field and technologies as used for object detection and recognition.
  • the system of the present disclosure improves upon a three- stage cascaded classifier for target recognition in EO and IR videos from static or moving platforms.
  • the first stage is an Integral Channel Features (ICF) detector 300 that takes in video and runs a fast detection (e.g., greater than 15 frames per second) to provide high-confidence candidate target regions as bounding boxes and scores (e.g.,“MAN” or person target (or other object of interest)) in the video.
  • ICF Integral Channel Features
  • the ICF is based on agglomeration of“channel features” and the training of small decision trees using these features.
  • Basic features can be thought of as maps from raw pixel values (RGB/IR) to more informative features such as oriented gradients, Haar features, difference of regions or simply color-space transformations.
  • the output of the ICF detector 300 are detected target box locations and associated confidences.
  • the ICF detector 300 computes channel feature vectors for image frames of the video, and each image frame, an ICF classifier is applied at multiple image scales and across the entire image frame.
  • the system of the present disclosure adds a target size filter 302 to the system.
  • the target size filter 302 is applied to the output of the first stage to affect the confidence scores based on expected versus detected target size box.
  • the candidate bounding boxes with the modified confidence scores are then fed through a second stage, which is a convolutional neural net (CNN) classifier 304 that outputs target class, location and confidences.
  • CNN convolutional neural net
  • the CNN classifier 304 is implemented as interacting software modules comprising a CNN interface and a CNN server (e.g., one or more processors and corresponding memory), wherein the CNN interface displays results received from the CNN server.
  • the CNN interface takes the candidate target box information from the ICF detector 300 and extracts the image regions from the input video, and hands that off to the CNN server for classification.
  • the CNN interface can display it live and also log the results to disk files and provide the output target boxes for further processing.
  • the third stage is a multi-target tracker (MTT) 306 that tracks the target boxes from the CNN stage (i.e., the CNN classifier 304) for final target classification, locations and confidence scores.
  • the tracker results are fed to a comparator for further processing by the CNN stage.
  • the system empirically estimates the bottom of object (e.g., feet location) versus object height (i.e., top of object) in the image rather than constructing an analytical formula.
  • the image frame 500 e.g., 640x480 pixels
  • N e.g. 16
  • the ground truth objects are grouped into N bins 504 according to where the bottom (or feet) of the ground truth box is located in the images.
  • the system can estimate their height distribution. For example, a normal (Gaussian) distribution with mean and standard deviation (m, s) is used to represent this distribution. However, it is often convenient to illustrate the distribution via histogram plots, as described and illustrated.
  • the detector or confidence score coming out of the ICF detector 300 is modified.
  • Post-processing is used to evaluate this approach in terms of its impact on system receiver operating characteristics (ROC) so that no real detector or classification processing is required.
  • This evaluation approach only involves the first two stages (i.e., ICF detector 300 and CNN Classifier 304) along with the target size filter 302, and does not involve the third stage (i.e., MTT 306).
  • the system computes and applies a multiplicative weight factor (wj) on the detection confidence score based on the location and height of the detection. Two methods to compute wf are described further below.
  • the system learns to predict object class by combining image and location/size information to train an alternative neural network to produce classification results.
  • the top row processing employs the same deep convolution network (CNN-l) classifier 304 as in Stage 2, whereas the second row employs a modified convolution network (CNN-2) classifier 400.
  • the modified convolution network classifier 400 outputs target class, location and confidences, which are fused 402 with that from the CNN classifier 304 and provided to the MTT 306 that tracks the target boxes for final target classification, locations and confidence scores.
  • the modified convolution network classifier is expanded and further
  • the 1024- D (dimensional) features 408 from the final convolution layer i.e., deep convolution layers 406 are padded with the target size and location 410 and fed to the fully connected (FC) layer 412 before the classifier layer.
  • the modified CNN-2 400 can be used alongside of the original CNN-l 304 and the results of CNN-l 304 and CNN-2 400 can be fused 402 to arrive at a final decision.
  • CNN-l 304 can be replaced with CNN-2 400 while maintaining the same processing flow.
  • Fusion 402 can be carried out by combining the probability distributions of CNN-l and CNN-2 over the set of classes to be classified; for example, a simple average of the weights of the classes from the two CNN’s and the renormalize to make the sum of weights to 1.0.
  • the first method of modifying the confidence score uses a weighted Gaussian according to the following equation:
  • h denotes a height of a detected object in the image whose detection confidence score is to be modified
  • s denote mean and standard deviation, respectively, of object height distribution in the corresponding image and bin.
  • exp(.) denotes the exponential function.
  • N ⁇ 1, 2, 3, 4 ... ⁇ is a multiplier to relax the detection size constraints.
  • the multiplicative weight factor (wf) is then multiplied with the original score to derive the new and modified confidence score (i.e., new score).
  • FIGs. 6A and 6B show plots 604 depicting the target height 600 distributions of 88 training sequences of the IR data set from side-facing (shown in FIG. 6A) and front-facing (shown in FIG. 6B) sensors, respectively, based on ground truth (GT) information from human annotation.
  • GT ground truth
  • the height distribution 600 is collected in 16 horizontal bands (depicted as 16 plots in each of FIGs. 6 A and 6B) across image height of 480 rows, and plotted as 25-bin histograms with their Gaussian approximation (mean and standard deviation).
  • the histograms for each band are labeled with the image rows it covers. Where there are no histograms plotted means there is not sufficient GT target samples to support the histogram estimation for corresponding bands.
  • FIGs. 6A and 6B illustrate two things. First, when a histogram plot is missing in the corresponding bin, targets appearing in those bins are unlikely regardless of target height. Second, for the bins with histogram plots, the histograms are Gaussian-like.
  • the underlying target heights can be modeled well by Gaussian distributions. As such, once an estimated target height for a particular bin is estimated, the likelihood of the target detection to be true can be estimated based on such distributions.
  • the second method of modifying the confidence score uses a weighted gate according to the following equation:
  • FIG. 3 The embodiment as illustrated in FIG. 3 has been implemented and tested for the task of recognizing dismounts (“MAN” target) and their activities in both EO and IR videos from stationary and moving ground vehicles.
  • FIGs. 6 A and 6B a set of IR video sequences was collected from a moving vehicle and the histogram distributions of the height 600 of the ground- truth (GT) boxes for objects 602 were plotted to form a collection of collection of height histogram plots 604. Since the sensors used for side-facing IR sensor and front-facing IR sensor sequences have different tilt angles, the histogram gathering was performed separately for the hlco and h2co sequences. These are shown in FIGs. 6A and 6B discussed before. As can be seen from the histogram plots in FIGs.
  • the GT height 600 decreases monotonically from the bottom of the image (rows 450-479) 606 towards the top, and ends in rows 30-59 608 with a mean of 23.9 pixels for hlco, and rows 180-209 610 with a mean of 17.2 for h2co.
  • the greatest concentration of GT MAN-* objects are located in the 3-4 bands just below the bands with shortest targets listed above.
  • FIG. 7 is a graph illustrating a comparison of detection scores weighted and unweighted for 30 IRtest sequences in both Pre-CNN (Stage 1) and Post-CNN (Stage 2) receiver operating characteristic (ROC) curves. Only the Gaussian window approach is shown since the Gated approach (Method 2) achieves almost identical results.
  • FIGs. 9A and 9B are graphs (i.e., collection of height histogram plots 900) illustrating height distribution of GT“MAN*” objects in 88 training sequences for side-facing color (or EO) sensors, FIG. 9A) and front-facing EO sensors, FIG. 9B).
  • the height distribution is collected in 16 horizontal bands across image height of 768 rows, and plotted as 25-bin histograms with the Gaussian approximation (from mean and stand deviation) overlaid.
  • the histograms for each band are labeled with the image rows it covers.
  • the invention described herein allows EO or IR vision-based object/target recognition in real-time even on small, low power, low cost platform (such ETAVs and ETGVs). This approach is also amenable for implementation on emerging spiking neuromorphic hardware, for example, a neuromorphic chip.
  • the system according to embodiments of the present disclosure can be used in intelligence, surveillance, and reconnaissance (ISR) operations, border security, and mission safety, such as for UAV based surveillance, human activity detection, threat detection, and distributed mobile operations.
  • ISR intelligence, surveillance, and reconnaissance
  • the classified object output can be used to alert (via audible, tactile, and/or visual alert, etc.) the driver/team that there is a high-confidence“MAN” target and its location.
  • the vehicle can then take evasive action by causing the vehicle to change route, etc., or attack that target after manual confirmation of its danger.
  • a remotely operated vehicle it can also provide a similar alert.
  • the system can be embedded in autonomous robotic vehicles, such as UAVs and UGVs, and self-driving vehicles.
  • autonomous robotic vehicles such as UAVs and UGVs
  • self-driving vehicles For instance, in a self- driving vehicle, the system can be used for collision avoidance.
  • the system detects an object (target) in its path (e.g., a pedestrian, another vehicle)
  • an alert is sent to the vehicle operating system to cause the vehicle to perform a braking operation.
  • the alert may signal that the vehicle operating system should perform a swerving motion around the object (target), involving steering and accelerating operations or any other operations as required to provide for collision avoidance.
  • the object detected may be a road sign, such as a stop sign.
  • an alert can be sent to the vehicle operating system causing the vehicle to brake or otherwise adhere to the message as conveyed by the road sign. Therefore and as noted above, the system and process described herein can be used to control a variety of devices, such as causing said device to perform an operation or physical maneuver.
  • FIG. 11 is a flow diagram illustrating using a processor 1100 to control a device 1102 based on classification of an object as a target.
  • devices 1102 that can be controlled via the processor 1100 and the classification of the target object include a vehicle or a vehicle component, such as a brake, acceleration/deceleration controls, a steering mechanism, suspension, or safety device (e.g., airbags, seatbelt tensioners, etc.), or any combination thereof.
  • the vehicle could be an unmanned aerial vehicle (UAV), an autonomous ground vehicle, or a human operated vehicle controlled either by a driver or by a remote operator.
  • UAV unmanned aerial vehicle
  • control of other device types is also possible given classification of an object as a target and the corresponding circumstances in which the system is employed.

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Abstract

La présente invention concerne un système de reconnaissance d'objets. À l'aide d'un détecteur de caractéristiques de canal intégral (ICF), le système extrait une région cible candidate (ayant un score de confiance original associé représentant un objet candidat) d'une image d'entrée d'une scène entourant une plateforme. Un score de confiance modifié est généré sur la base d'un emplacement et d'une hauteur de détection de l'objet candidat. Les régions cibles candidates sont classées sur la base du score de confiance modifié à l'aide d'un classificateur de réseau neuronal à convolutions (CNN) entraîné, ce qui donne des objets classés. Les objets classés sont suivis à l'aide d'un suiveur de cibles multiples pour obtenir une classification finale de chaque objet classé en tant que cible ou non-cible. Si l'objet classé est une cible, un dispositif peut être commandé sur la base de la cible.
PCT/US2019/018119 2018-04-17 2019-02-14 Système de détection et de reconnaissance d'objets en temps réel à l'aide de caractéristiques d'image et de taille WO2019203921A1 (fr)

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EP19789101.3A EP3782075A4 (fr) 2018-04-17 2019-02-14 Système de détection et de reconnaissance d'objets en temps réel à l'aide de caractéristiques d'image et de taille

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Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111275054A (zh) * 2020-01-16 2020-06-12 北京迈格威科技有限公司 图像处理方法、装置、电子设备及存储介质
CN112560726A (zh) * 2020-12-22 2021-03-26 北京百度网讯科技有限公司 目标检测置信度确定方法、路侧设备及云控平台
CN112633323A (zh) * 2020-11-26 2021-04-09 成都佳发安泰教育科技股份有限公司 一种用于教室的姿态检测方法和系统
CN117075130A (zh) * 2023-07-07 2023-11-17 中国电子科技集团公司第三十八研究所 低慢小目标激光跟踪装置及其工作方法

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20100021010A1 (en) * 2008-07-25 2010-01-28 Gm Global Technology Operations, Inc. System and Method for detecting pedestrians
KR20150017762A (ko) * 2012-06-14 2015-02-17 도요타지도샤가부시키가이샤 식별기 생성 장치 및 패턴 검출 장치
CN105913003A (zh) * 2016-04-07 2016-08-31 国家电网公司 一种多特征多模型的行人检测方法
CN107092883A (zh) * 2017-04-20 2017-08-25 上海极链网络科技有限公司 物体识别追踪方法
US20170364757A1 (en) * 2016-06-20 2017-12-21 Delphi Technologies, Inc. Image processing system to detect objects of interest

Family Cites Families (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US9613273B2 (en) * 2015-05-19 2017-04-04 Toyota Motor Engineering & Manufacturing North America, Inc. Apparatus and method for object tracking
US9582895B2 (en) * 2015-05-22 2017-02-28 International Business Machines Corporation Real-time object analysis with occlusion handling
CN105760858A (zh) * 2016-03-21 2016-07-13 东南大学 一种基于类Haar中间层滤波特征的行人检测方法及装置
WO2017192629A1 (fr) * 2016-05-02 2017-11-09 The Regents Of The University Of California Système et procédé d'estimation de paramètres de perfusion au moyen de l'imagerie médicale
US10083369B2 (en) * 2016-07-01 2018-09-25 Ricoh Company, Ltd. Active view planning by deep learning
CN107273832B (zh) * 2017-06-06 2020-09-22 青海省交通科学研究院 基于积分通道特征与卷积神经网络的车牌识别方法及系统
CN110348428B (zh) * 2017-11-01 2023-03-24 腾讯科技（深圳）有限公司 眼底图像分类方法、装置及计算机可读存储介质

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20100021010A1 (en) * 2008-07-25 2010-01-28 Gm Global Technology Operations, Inc. System and Method for detecting pedestrians
KR20150017762A (ko) * 2012-06-14 2015-02-17 도요타지도샤가부시키가이샤 식별기 생성 장치 및 패턴 검출 장치
CN105913003A (zh) * 2016-04-07 2016-08-31 国家电网公司 一种多特征多模型的行人检测方法
US20170364757A1 (en) * 2016-06-20 2017-12-21 Delphi Technologies, Inc. Image processing system to detect objects of interest
CN107092883A (zh) * 2017-04-20 2017-08-25 上海极链网络科技有限公司 物体识别追踪方法

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
See also references of EP3782075A4 *

Cited By (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111275054A (zh) * 2020-01-16 2020-06-12 北京迈格威科技有限公司 图像处理方法、装置、电子设备及存储介质
CN111275054B (zh) * 2020-01-16 2023-10-31 北京迈格威科技有限公司 图像处理方法、装置、电子设备及存储介质
CN112633323A (zh) * 2020-11-26 2021-04-09 成都佳发安泰教育科技股份有限公司 一种用于教室的姿态检测方法和系统
CN112633323B (zh) * 2020-11-26 2024-04-30 成都佳发安泰教育科技股份有限公司 一种用于教室的姿态检测方法和系统
CN112560726A (zh) * 2020-12-22 2021-03-26 北京百度网讯科技有限公司 目标检测置信度确定方法、路侧设备及云控平台
KR20210133174A (ko) * 2020-12-22 2021-11-05 아폴로 인텔리전트 커넥티비티 (베이징) 테크놀로지 씨오., 엘티디. 목표 탐지 신뢰도를 결정하는 방법 및 장치, 전자 기기, 저장 매체, 노변 기기, 클라우드 제어 플랫폼, 컴퓨터 프로그램 제품
JP2022020673A (ja) * 2020-12-22 2022-02-01 阿波▲羅▼智▲聯▼（北京）科技有限公司 ターゲット検出の信頼度を確定する方法、装置、電子機器、記憶媒体、路側機、クラウド制御プラットフォーム及びコンピュータプログラム
EP3923187A3 (fr) * 2020-12-22 2022-05-18 Apollo Intelligent Connectivity (Beijing) Technology Co., Ltd. Procédé permettant de déterminer le niveau de confiance de la détection d'une cible, dispositif routier et plateforme de commande en nuage
JP7263478B2 (ja) 2020-12-22 2023-04-24 阿波▲羅▼智▲聯▼（北京）科技有限公司 ターゲット検出の信頼度を確定する方法、装置、電子機器、記憶媒体、路側機、クラウド制御プラットフォーム及びコンピュータプログラム
CN112560726B (zh) * 2020-12-22 2023-08-29 阿波罗智联(北京)科技有限公司 目标检测置信度确定方法、路侧设备及云控平台
KR102604426B1 (ko) * 2020-12-22 2023-11-22 아폴로 인텔리전트 커넥티비티 (베이징) 테크놀로지 씨오., 엘티디. 목표 탐지 신뢰도를 결정하는 방법 및 장치, 전자 기기, 저장 매체, 노변 기기, 클라우드 제어 플랫폼, 컴퓨터 프로그램 제품
CN117075130A (zh) * 2023-07-07 2023-11-17 中国电子科技集团公司第三十八研究所 低慢小目标激光跟踪装置及其工作方法

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