Attribute-based alert ranking for alert adjudication
First Claim
1. A method for prioritizing the adjudication of object alerts as a function of relative visual attribute values, the method comprising:
- in response to detecting an object that is discernible and static within an image scene of a video data input, a processing unit;
generating an alert;
tracking the detected object over a tracking period of time;
extracting low-level image features from the video data input that are relevant to the object over the tracking time period;
learning and ranking relative strengths of each of a plurality of attributes from the extracted features, wherein the plurality of attributes comprises an abandonment attribute that indicates a level of likelihood of abandonment of the object, a foregroundness attribute that quantifies a level of separation of foreground image data of the object from a background model of the image scene, and a staticness attribute that quantifies a level of stability of dimensions of a bounding box of the object over time; and
prioritizing the alert relative to other alerts according to a learned importance or relevance value generated from the relative abandonment, foregroundness and staticness attribute strengths.
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Accused Products
Abstract
Alerts to object behaviors are prioritized for adjudication as a function of relative values of abandonment, foregroundness and staticness attributes. The attributes are determined from feature data extracted from video frame image data. The abandonment attribute indicates a level of likelihood of abandonment of an object. The foregroundness attribute quantifies a level of separation of foreground image data of the object from a background model of the image scene. The staticness attribute quantifies a level of stability of dimensions of a bounding box of the object over time. Alerts are also prioritized according to an importance or relevance value that is learned and generated from the relative abandonment, foregroundness and staticness attribute strengths.
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Citations
21 Claims
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1. A method for prioritizing the adjudication of object alerts as a function of relative visual attribute values, the method comprising:
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in response to detecting an object that is discernible and static within an image scene of a video data input, a processing unit; generating an alert; tracking the detected object over a tracking period of time; extracting low-level image features from the video data input that are relevant to the object over the tracking time period; learning and ranking relative strengths of each of a plurality of attributes from the extracted features, wherein the plurality of attributes comprises an abandonment attribute that indicates a level of likelihood of abandonment of the object, a foregroundness attribute that quantifies a level of separation of foreground image data of the object from a background model of the image scene, and a staticness attribute that quantifies a level of stability of dimensions of a bounding box of the object over time; and prioritizing the alert relative to other alerts according to a learned importance or relevance value generated from the relative abandonment, foregroundness and staticness attribute strengths. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10)
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11. A system that automatically prioritizes the adjudication of object alerts as a function of relative visual attribute values, the system comprising:
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a processing unit in communication with a computer readable memory and a tangible computer-readable storage medium; wherein the processing unit executes program instructions stored on the tangible computer-readable storage medium via the computer readable memory and, in response to an indication that an object is discernible and static within an image scene of a video data input; tracks the detected object over a tracking period of time; extracts low-level image features from the video data input that are relevant to the object over the tracking time period; learns and ranks relative strengths of each of a plurality of attributes from the extracted features, wherein the plurality of attributes comprises an abandonment attribute that indicates a level of likelihood of abandonment of the object, a foregroundness attribute that quantifies a level of separation of foreground image data of the object from a background model of the image scene, and a staticness attribute that quantifies a level of stability of dimensions of a bounding box of the object over time; and prioritizes the alert relative to other alerts according to a learned importance or relevance value generated from the relative abandonment, foregroundness and staticness attribute strengths. - View Dependent Claims (12, 13, 14, 15)
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16. A computer program product for automatically prioritizing the adjudication of object alerts as a function of relative visual attribute values, the 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; track a detected object over a tracking period of time in response to an indication that the object is discernible and static within an image scene of a video data input; extract low-level image features from the video data input that are relevant to the object over the tracking time period; learn and rank relative strengths of each of plurality of attributes from the extracted features, wherein the plurality of attributes comprises an abandonment attribute that indicates a level of likelihood of abandonment of the object, a foregroundness attribute that quantifies a level of separation of foreground image data of the object from a background model of the image scene, and a staticness attribute that quantifies a level of stability of dimensions of a bounding box of the object over time; and prioritize the alert relative to other alerts according to a learned importance or relevance value generated from the relative abandonment, foregroundness and staticness attribute strengths. - View Dependent Claims (17, 18, 19, 20, 21)
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Specification