ANOMALOUS STATIONARY OBJECT DETECTION AND REPORTING

• US 20180084225A1
• Filed: 06/29/2017
• Published: 03/22/2018
• Est. Priority Date: 06/29/2012
• Status: Active Grant

• Current Assignee: Intellective Ai, Inc.
• Original Assignee: Omni AI, Inc.
• Inventors: XU, Gang, COBB, Wesley Kenneth
• Granted Patent: US 10,257,466 B2
• Time in Patent Office: Days
• CPC Class Codes: G06F 18/2433 Single-class perspective, e.g. one-against-all classification; Novelty detection; Outlier detection
• CPC Class Codes: G06V 10/764 using classification, e.g. of video objects
• CPC Class Codes: H04N 7/18 Closed-circuit television [CCTV] systems, i.e. systems in which the video signal is not broadcast
• CPC Class Codes: H04N 7/188 Capturing isolated or intermittent images triggered by the occurrence of a predetermined event, e.g. an object reaching a predetermined position signal generation from motion picture films H04N5/253

Abstract

Techniques are disclosed for analyzing a scene depicted in an input stream of video frames captured by a video camera. The techniques include receiving data for an object within the scene and determining whether the object has remained substantially stationary within the scene for at least a threshold period. If the object is determined to have remained stationary for at least the threshold period, a rareness score is calculated for the object to indicate a likelihood of the object being stationary to an observed degree at an observed location. The rareness score may use a learning model to take into account previous stationary and/or non-stationary behavior of objects within the scene. In general, the learning model may be updated based on observed stationary and/or non-stationary behaviors of the objects. If the rareness score meets reporting conditions, the stationary object event may be reported.

Claims

1. A computer-implemented method, comprising

analyzing, via one or more processors, an input stream of captured video frames, the captured video frames defining at least one scene;

determining, via the one or more processors, if an object has remained at a specified location in the at least one scene for at least a threshold period;

determining, via the one or more processors, if the object has remained substantially stationary within the at least one scene during the threshold period;

determining, via the one or more processors, a rareness score for the object if the object is determined to have remained substantially stationary within the at least one scene and remained at the specified location within the at least one scene for at least the threshold period, the rareness score determined based at least on a learned model of previous object behavior within the at least one scene; and

generating, via the one or more processors, an alert upon determining the rareness score exceeds a threshold.

2-20. -20. (canceled)