ESTIMATOR IDENTIFIER COMPONENT FOR BEHAVIORAL RECOGNITION SYSTEM
First Claim
1. A method for analyzing an object being tracked in a sequence of video frames, comprising:
- receiving a representation of the tracked object, as depicted by a current video frame, of the sequence of video frames;
evaluating the representation of the tracked object using at least a first classifier and a second classifier, wherein the first classifier is configured to determine a first classification score indicating whether the tracked object depicts an instance of a first classification type, and wherein the second classifier is configured to determine a second classification score indicating whether the tracked object depicts an instance of a second classification type;
determining a final classification value for the tracked object, based on the first and second classification scores; and
passing the final classification value for the tracked objects to a machine learning engine configured to identify patterns of behavior engaged in by the tracked object, based at least in part on the final classification value.
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Accused Products
Abstract
An estimator/identifier component for a computer vision engine of a machine-learning based behavior-recognition system is disclosed. The estimator/identifier component may be configured to classify an object being one of two or more classification types, e.g., as being a vehicle or a person. Once classified, the estimator/identifier may evaluate the object to determine a set of kinematic data, static data, and a current pose of the object. The output of the estimator/identifier component may include the classifications assigned to a tracked object, as well as the derived information and object attributes.
101 Citations
25 Claims
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1. A method for analyzing an object being tracked in a sequence of video frames, comprising:
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receiving a representation of the tracked object, as depicted by a current video frame, of the sequence of video frames; evaluating the representation of the tracked object using at least a first classifier and a second classifier, wherein the first classifier is configured to determine a first classification score indicating whether the tracked object depicts an instance of a first classification type, and wherein the second classifier is configured to determine a second classification score indicating whether the tracked object depicts an instance of a second classification type; determining a final classification value for the tracked object, based on the first and second classification scores; and passing the final classification value for the tracked objects to a machine learning engine configured to identify patterns of behavior engaged in by the tracked object, based at least in part on the final classification value. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9)
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10. A computer-readable storage medium containing a program which, when executed by a processor, performs an operation for analyzing an object being tracked in a sequence of video frames, the operation comprising:
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receiving a representation of the tracked object, as depicted by a current video frame, of the sequence of video frames; evaluating the representation of the tracked object using at least a first classifier and a second classifier, wherein the first classifier is configured to determine a first classification score indicating whether the tracked object depicts an instance of a first classification type, and wherein the second classifier is configured to determine a second classification score indicating whether the tracked object depicts an instance of a second classification type; determining a final classification value for the tracked object, based on the first and second classification scores; and passing the final classification value for the tracked objects to a machine learning engine configured to identify patterns of behavior engaged in by the tracked object, based at least in part on the final classification value. - View Dependent Claims (11, 12, 13, 14, 15, 16, 17, 18)
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19. A system comprising,
a video input source configured to provide a sequence of video frames, each depicting a scene; -
a processor; and a memory containing a computer vision engine, which when executed by the processor is configured to perform an operation for analyzing an object being tracked in a sequence of video frames, the operation comprising; receiving a representation of the tracked object, as depicted by a current video frame, of the sequence of video frames, evaluating the representation of the tracked object using at least a first classifier and a second classifier, wherein the first classifier is configured to determine a first classification score indicating whether the tracked object depicts an instance of a first classification type, and wherein the second classifier is configured to determine a second classification score indicating whether the tracked object depicts an instance of a second classification type, determining a final classification value for the tracked object, based on the first and second classification scores, and passing the final classification value for the tracked objects to a machine learning engine configured to identify patterns of behavior engaged in by the tracked object, based at least in part on the final classification value. - View Dependent Claims (20, 21, 22, 23, 24, 25)
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Specification