Method and system for tracking and behavioral monitoring of multiple objects moving through multiple fields-of-view
First Claim
Patent Images
1. A computerized method of video analysis comprising:
- receiving, at a computerized receiving device, a plurality of series of video frames generated by a plurality of image sensors, each image sensor having a field-of-view that monitors a portion of a monitored environment;
concurrently tracking, using a tracking module, a plurality of objects with respect to the monitored environment as the objects move among fields-of-view, wherein the tracking is based at least in part on a transition probability table, in which a first axis of the table represents a first set of image regions within video frames generated by the image sensors at a first point in time, a second axis of the table represents a second set of image regions within video frames generated by the image sensors at a second point in time, and each entry in the table represents a likelihood that one of the plurality of objects included in the first-axis image region corresponding to the entry will transition by the second time period to the second-axis image region corresponding to the entry;
storing a plurality of blob states over time, each state including a number of objects included in the blob and a blob signature, wherein the transition probability table comprises probabilities that objects within one blob at one instant in time correspond to objects within other blobs at other instants in time; and
predicting, concurrently with the tracking, an image sensor in the plurality of image sensors having a field-of-view in which at least one of the plurality of objects will be located at the second point in time, wherein the prediction is based on the presence of the at least one of the plurality of objects in a region in the first set of image regions at the first point in time and on the transition probability table, independent of calibration among the image sensors and the monitored environment.
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Abstract
A computerized method of video analysis that includes receiving several series of video frames generated by a number of image sensors. Each image sensor has its own field-of-view which can, but does not have to, overlap with another image sensor'"'"'s field-of-view. The image sensors monitor a portion of a monitored environment. The computerized method also includes concurrently tracking, independent of calibration, multiple objects within the monitored environment as the objects move between fields-of-view, and multiple objects within one field-of-view. The tracking is based on the plurality of received series of video frames.
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Citations
29 Claims
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1. A computerized method of video analysis comprising:
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receiving, at a computerized receiving device, a plurality of series of video frames generated by a plurality of image sensors, each image sensor having a field-of-view that monitors a portion of a monitored environment; concurrently tracking, using a tracking module, a plurality of objects with respect to the monitored environment as the objects move among fields-of-view, wherein the tracking is based at least in part on a transition probability table, in which a first axis of the table represents a first set of image regions within video frames generated by the image sensors at a first point in time, a second axis of the table represents a second set of image regions within video frames generated by the image sensors at a second point in time, and each entry in the table represents a likelihood that one of the plurality of objects included in the first-axis image region corresponding to the entry will transition by the second time period to the second-axis image region corresponding to the entry; storing a plurality of blob states over time, each state including a number of objects included in the blob and a blob signature, wherein the transition probability table comprises probabilities that objects within one blob at one instant in time correspond to objects within other blobs at other instants in time; and predicting, concurrently with the tracking, an image sensor in the plurality of image sensors having a field-of-view in which at least one of the plurality of objects will be located at the second point in time, wherein the prediction is based on the presence of the at least one of the plurality of objects in a region in the first set of image regions at the first point in time and on the transition probability table, independent of calibration among the image sensors and the monitored environment. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 20, 27, 29)
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13. A computerized system for video analysis comprising:
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a receiving module configured to receive a plurality of series of video frames, the series of video frames generated over time by a plurality of image sensors which monitor portions of a monitored environment and have a field-of-view; and a calibration-independent tracking module in communication with the receiving module and configured to i) concurrently track a plurality of objects with respect to within the monitored environment as the objects move among fields-of-view, wherein the tracking is based at least in part on a transition probability table, in which a first axis of the table represents a first set of image regions within frames generated by the image sensors at a first point in time, a second axis of the table represents a second set of image regions within video frames generated by the image sensors at a second point in time, and each entry in the table represents a likelihood that one of the plurality of objects included in the first-axis image region corresponding to the entry will transition by the second time period to the second-axis image region corresponding to the entry, ii) store a plurality of blob states over time, each state including a number of objects included in the blob and a blob signature, wherein the transition probability table comprises probabilities that objects within one blob at one instant in time correspond to objects within other blobs at other instants in time, and iii) predict, concurrently with the tracking, an image sensor in the plurality of image sensors having a field-of-view in which at least one of the plurality of objects will be located at the second point in time, wherein the prediction is based on the presence of the at least one of the plurality of objects in a region in the first set of image regions at the first point in time and on the transition probability table, independent of calibration among the image sensors and the monitored environment. - View Dependent Claims (14, 21, 28)
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15. A system for monitoring parking lot security comprising:
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a receiving module configured to receive a plurality of series of video frames, the series of video frames generated over time by a plurality of image sensors which monitor portions of a monitored environment and have a field-of-view; a calibration-independent tracking module in communication with the receiving module and configured to i) concurrently track a plurality of objects with respect to within the monitored environment as the objects move among fields-of-view, wherein the tracking is based at least in part on a transition probability table, in which a first axis of the table represents a first set of image regions within video frames generated by the image sensors at a first point in time, a second axis of the table represents a second set of image regions within video frames generated by the image sensors at a second point in time, and each entry in the table represents a likelihood that one of the plurality of objects included in the first-axis image region corresponding to the entry will transition by the second time period to the second-axis image region corresponding to the entry, ii) store a plurality of blob states over time, each state including a number of objects included in the blob and a blob signature, wherein the transition probability table comprises probabilities that objects within one blob at one instant in time correspond to objects within other blobs at other instants in time, and iii) predict, concurrently with tracking the plurality of objects, an image sensor in the plurality of image sensors having a field-of-view in which at least one of the plurality of objects will be located at the second point in time, wherein the prediction is based on the presence of the at least one of the plurality of objects in a region in the first set of image regions at the first point in time and on the transition probability table, independent of calibration among the image sensors and the monitored environment, and iii) concurrently track the plurality of objects within one field-of-view based on at least some of the received series of video frames and independent of calibration among the image sensors and the monitored environment, the tracking module outputting tracking metadata; and a rules engine utilizing a parking lot security rule set configured to receive and evaluate the tracking metadata. - View Dependent Claims (22)
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16. A system for property theft detection comprising:
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a receiving module configured to receive a plurality of series of video frames, the series of video frames generated over time by a plurality of image sensors which monitor portions of a monitored environment and have a field-of-view; a calibration-independent tracking module in communication with the receiving module and configured to i) concurrently track a plurality of objects with respect to within the monitored environment as the objects move among fields-of-view, wherein the tracking is based at least in part on a transition probability table, in which a first axis of the table represents a first set of image regions within video frames generated by the image sensors at a first point in time, a second axis of the table represents a second set of image regions within video frames generated by the image sensors at a second point in time, and each entry in the table represents a likelihood that one of the plurality of objects included in the first-axis image region corresponding to the entry will transition by the second time period to the second-axis image region corresponding to the entry, ii) store a plurality of blob states over time, each state including a number of objects included in the blob and a blob signature, wherein the transition probability table comprises probabilities that objects within one blob at one instant in time correspond to objects within other blobs at other instants in time, and iii) predict, concurrently with tracking the plurality of objects, an image sensor in the plurality of image sensors having a field-of-view in which at least one of the plurality of objects will be located at the second point in time, wherein the prediction is based on the presence of the at least one of the plurality of objects in a region in the first set of image regions at the first point in time and on the transition probability table, independent of calibration among the image sensors and the monitored environment and iii) concurrently track the plurality of objects within one field-of-view based on at least some of the received series of video frames and independent of calibration among the image sensors and the monitored environment, the tracking module outputting tracking metadata; and a rules engine utilizing a theft detection rule set configured to receive and evaluate the tracking metadata. - View Dependent Claims (23)
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17. A system for child hazard detection comprising:
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a receiving module configured to receive a plurality of series of video frames, the series of video frames generated over time by a plurality of image sensors which monitor portions of a monitored environment and have a field-of-view; a calibration-independent tracking module in communication with the receiving module and configured to i) concurrently track a plurality of objects with respect to within the monitored environment as the objects move among fields-of-view, wherein the tracking is based at least in part on a transition probability table, in which a first axis of the table represents a first set of image regions within video frames generated by the image sensors at a first point in time, a second axis of the table represents a second set of image regions within video frames generated by the image sensors at a second point in time, and each entry in the table represents a likelihood that one of the plurality of objects included in the first-axis image region corresponding to the entry will transition by the second time period to the second-axis image region corresponding to the entry, ii) store a plurality of blob states over time, each state including a number of objects included in the blob and a blob signature, wherein the transition probability table comprises probabilities that objects within one blob at one instant in time correspond to objects within other blobs at other instants in time, and iii) predict, concurrently with tracking the plurality of objects, an image sensor in the plurality of image sensors having a field-of-view in which at least one of the plurality of objects will be located at the second point in time, wherein the prediction is based on the presence of the at least one of the plurality of objects in a region in the first set of image regions at the first point in time and on the transition probability table, independent of calibration among the image sensors and the monitored environment and iii) concurrently track the plurality of objects within one field-of-view based on at least some of the received series of video frames and independent of calibration among the image sensors and the monitored environment, the tracking module outputting tracking metadata; and a rules engine utilizing a child safety rule set configured to receive and evaluate the tracking metadata. - View Dependent Claims (24)
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18. A system for public safety monitoring comprising:
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a receiving module configured to receive a plurality of series of video frames, the series of video frames generated over time by a plurality of image sensors which monitor portions of a monitored environment and have a field-of-view; a calibration-independent tracking module in communication with the receiving module and configured to i) concurrently track a plurality of objects with respect to within the monitored environment as the objects move among fields-of-view, wherein the tracking is based at least in part on a transition probability table, in which a first axis of the table represents a first set of image regions within video frames generated by the image sensors at a first point in time, a second axis of the table represents a second set of image regions within video frames generated by the image sensors at a second point in time, and each entry in the table represents a likelihood that one of the plurality of objects included in the first-axis image region corresponding to the entry will transition by the second time period to the second-axis image region corresponding to the entry, ii) store a plurality of blob states over time, each state including a number of objects included in the blob and a blob signature, wherein the transition probability table comprises probabilities that objects within one blob at one instant in time correspond to objects within other blobs at other instants in time, and iii) predict, concurrently with tracking the plurality of objects, an image sensor in the plurality of image sensors having a field-of-view in which at least one of the plurality of objects will be located at the second point in time, wherein the prediction is based on the presence of the at least one of the plurality of objects in a region in the first set of image regions at the first point in time and on the transition probability table, independent of calibration among the image sensors and the monitored environment and iii) concurrently track the plurality of objects within one field-of-view based on at least some of the received series of video frames and independent of calibration among the image sensors and the monitored environment, the tracking module outputting tracking metadata; and a rules engine utilizing a public safety monitoring rule set configured to receive and evaluate the tracking metadata. - View Dependent Claims (25)
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19. A system for merchandizing and operations statistical analysis comprising:
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a receiving module configured to receive a plurality of series of video frames, the series of video frames generated over time by a plurality of image sensors which monitor portions of a monitored environment and have a field-of-view; a calibration-independent tracking module in communication with the receiving module and configured to i) concurrently track a plurality of objects with respect to within the monitored environment as the objects move among fields-of-view, wherein the tracking is based at least in part on a transition probability table, in which a first axis of the table represents a first set of image regions within video frames generated by the image sensors at a first point in time, a second axis of the table represents a second set of image regions within video frames generated by the image sensors at a second point in time, and each entry in the table represents a likelihood that one of the plurality of objects included in the first-axis image region corresponding to the entry will transition by the second time period to the second-axis image region corresponding to the entry, ii) store a plurality of blob states over time, each state including a number of objects included in the blob and a blob signature, wherein the transition probability table comprises probabilities that objects within one blob at one instant in time correspond to objects within other blobs at other instants in time, and iii) predict, concurrently with tracking the plurality of objects, an image sensor in the plurality of image sensors having a field-of-view in which at least one of the plurality of objects will be located at the second point in time, wherein the prediction is based on the presence of the at least one of the plurality of objects in a region in the first set of image regions at the first point in time and on the transition probability table, independent of calibration among the image sensors and the monitored environment and iii) concurrently track the plurality of objects within one field-of-view based on at least some of the received series of video frames and independent of calibration among the image sensors and the monitored environment, the tracking module outputting tracking metadata; and a rules engine utilizing a merchandizing and operations statistical rule set configured to receive and evaluate the tracking metadata. - View Dependent Claims (26)
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Specification