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 image features from the video data input 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 a foregroundness attribute and a staticness attribute;
identifying the detected object as one of a bag object, a people object and a visual artifact as a function of the learned and ranked relative strengths of the plurality of extracted feature attributes, wherein the bag object, the people object and the visual artifact are each associated with different combinations of values of learned and ranked relative strengths of the plurality of extracted feature attributes; and
prioritizing the alert relative to other alerts by ranking alerts generated from detected bag objects over alerts generated from detected people objects, and alerts generated from detected people object over alerts generated from detected visual artifacts.
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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
20 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 image features from the video data input 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 a foregroundness attribute and a staticness attribute; identifying the detected object as one of a bag object, a people object and a visual artifact as a function of the learned and ranked relative strengths of the plurality of extracted feature attributes, wherein the bag object, the people object and the visual artifact are each associated with different combinations of values of learned and ranked relative strengths of the plurality of extracted feature attributes; and prioritizing the alert relative to other alerts by ranking alerts generated from detected bag objects over alerts generated from detected people objects, and alerts generated from detected people object over alerts generated from detected visual artifacts. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12)
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13. 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; a computer readable memory in communication with the processing unit; and a computer-readable storage medium in 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, in response to an indication that an object is discernible and static within an image scene of a video data input; generates an alert; tracks the detected object over a tracking period of time; extracts image features from the video data input 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 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; identify the detected object as one of a bag object, a people object and a visual artifact as a function of the learned and ranked relative strengths of the plurality of extracted feature attributes, wherein the bag object, the people object and the visual artifact are each associated with different combinations of values of learned and ranked relative strengths of the plurality of extracted feature attributes; and prioritize the alert relative to other alerts by ranking alerts generated from detected bag objects over alerts generated from detected people objects, and alerts generated from detected people object over alerts generated from detected visual artifacts. - View Dependent Claims (14, 15, 16)
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17. 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; generate an alert; track the detected object over a tracking period of time; extract image features from the video data input over the tracking time period; learn and rank relative strengths of each of a plurality of attributes from the extracted features, wherein the plurality of attributes comprises 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; identify the detected object as one of a bag object, a people object and a visual artifact as a function of the learned and ranked relative strengths of the plurality of extracted feature attributes, wherein the bag object, the people object and the visual artifact are each associated with different combinations of values of learned and ranked relative strengths of the plurality of extracted feature attributes; and prioritize the alert relative to other alerts by ranking alerts generated from detected bag objects over alerts generated from detected people objects, and alerts generated from detected people object over alerts generated from detected visual artifacts. - View Dependent Claims (18, 19, 20)
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Specification