Object retrieval in video data using complementary detectors
First Claim
1. A method for automatic object retrieval from input video based on learned detectors, the method comprising:
- in response to a video stream input received from a fixed-camera surveillance video for analysis, a processing unit iteratively running different detectors of a plurality of pairs of complementary detectors in one each of subsequent frames of the surveillance video stream input;
collecting firings data for each of the run detectors per image frame location, until a threshold number of firings is reached by at least one of the run detectors; and
analyzing the frames from the surveillance video stream input to extract image attributes of vehicle objects by applying a subset of the run detectors that each reach the threshold number of firings in collecting the firings data for the image frame locations; and
wherein the detectors fire if an underlying vehicle image patch extracted from the motion blobs in a field of view of scene image data corresponds to image patches of the applied detectors.
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Accused Products
Abstract
Automatic object retrieval from input video is based on learned, complementary detectors created for each of a plurality of different motionlet clusters. The motionlet clusters are partitioned from a dataset of training vehicle images as a function of determining that vehicles within each of the scenes of the images in each cluster share similar two-dimensional motion direction attributes within their scenes. To train the complementary detectors, a first detector is trained on motion blobs of vehicle objects detected and collected within each of the training dataset vehicle images within the motionlet cluster via a background modeling process; a second detector is trained on each of the training dataset vehicle images within the motionlet cluster that have motion blobs of the vehicle objects but are misclassified by the first detector; and the training repeats until all of the training dataset vehicle images have been eliminated as false positives or correctly classified.
9 Citations
20 Claims
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1. A method for automatic object retrieval from input video based on learned detectors, the method comprising:
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in response to a video stream input received from a fixed-camera surveillance video for analysis, a processing unit iteratively running different detectors of a plurality of pairs of complementary detectors in one each of subsequent frames of the surveillance video stream input; collecting firings data for each of the run detectors per image frame location, until a threshold number of firings is reached by at least one of the run detectors; and analyzing the frames from the surveillance video stream input to extract image attributes of vehicle objects by applying a subset of the run detectors that each reach the threshold number of firings in collecting the firings data for the image frame locations; and wherein the detectors fire if an underlying vehicle image patch extracted from the motion blobs in a field of view of scene image data corresponds to image patches of the applied detectors. - View Dependent Claims (2, 3, 4, 5, 6, 7)
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8. A system, comprising:
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a processing unit; a computer readable memory in circuit communication with the processing unit; and a computer-readable storage medium in circuit communication with the processing unit; wherein the processing unit executes program instructions stored on the computer-readable storage medium via the computer readable memory and thereby; iteratively runs different detectors of a plurality of pairs of complementary detectors in one each of subsequent frames of a surveillance video stream input received from a fixed-camera surveillance video for analysis; collects firings data for each of the run detectors per image frame location until a threshold number of firings is reached by at least one of the run detectors; and analyzes the frames from the surveillance video stream input to extract image attributes of vehicle objects by applying a subset of the run detectors that each reach the threshold number of firings in collecting the firings data for the image frame locations; and wherein the detectors fire if an underlying vehicle image patch extracted from the motion blobs in a field of view of scene image data corresponds to image patches of the applied detectors. - View Dependent Claims (9, 10, 11, 12, 13, 14)
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15. A computer program product, comprising:
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a computer readable hardware storage device having computer readable program code embodied therewith, the computer readable program code comprising instructions for execution by a computer processing unit that cause the computer processing unit to; iteratively run different detectors of a plurality of pairs of complementary detectors in one each of subsequent frames of a surveillance video stream input received from a fixed-camera surveillance video for analysis; collect firings data for each of the run detectors per image frame location until a threshold number of firings is reached by at least one of the run detectors; and analyze the frames from the surveillance video stream input to extract image attributes of vehicle objects by applying a subset of the run detectors that each reach the threshold number of firings in collecting the firings data for the image frame locations; and wherein the detectors fire if an underlying vehicle image patch extracted from the motion blobs in a field of view of scene image data corresponds to image patches of the applied detectors. - View Dependent Claims (16, 17, 18, 19, 20)
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Specification