发明名称 Attribute-based alert ranking for alert adjudication
摘要 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.
申请公布号 US9330314(B2) 申请公布日期 2016.05.03
申请号 US201514948458 申请日期 2015.11.23
申请人 INTERNATIONAL BUSINESS MACHINES CORPORATION 发明人 Fan Quanfu;Pankanti Sharathchandra U.
分类号 G06K9/00;G06K9/66;G06T7/20;G06K9/62;G06K9/32 主分类号 G06K9/00
代理机构 Driggs, Hogg, Daugherty & Del Zoppo Co., LPA 代理人 Daugherty Patrick J.;Driggs, Hogg, Daugherty & Del Zoppo Co., LPA
主权项 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.
地址 Armonk NY US