Anomalous object interaction detection and reporting

Abstract

Techniques are disclosed for analyzing a scene depicted in an input stream of video frames captured by a video camera. The techniques include evaluating sequence pairs representing segments of object trajectories. Assuming the objects interact, each of the sequences of the sequence pair may be mapped to a sequence cluster of an adaptive resonance theory (ART) network. A rareness value for the pair of sequence clusters may be determined based on learned joint probabilities of sequence cluster pairs. A statistical anomaly model, which may be specific to an interaction type or general to a plurality of interaction types, is used to determine an anomaly temperature, and alerts are issued based at least on the anomaly temperature. In addition, the ART network and the statistical anomaly model are updated based on the current interaction.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS[0001]This application claims priority to U.S. provisional application having Ser. No. 61 / 666,458, filed on Jun. 29, 2012, which is hereby incorporated by reference in its entirety.BACKGROUND OF THE INVENTION[0002]1. Field of the Invention[0003]Embodiments of the invention provide techniques for analyzing a sequence of video frames. More particularly, to analyzing and learning behavior based on streaming video data, including detection and reporting of anomalous object interactions.[0004]2. Description of the Related Art[0005]Some currently available video surveillance systems provide simple object recognition capabilities. For example, a video surveillance system may be configured to classify a group of pixels (referred to as a “blob”) in a given frame as being a particular object (e.g., a person or vehicle). Once identified, a “blob” may be tracked from frame-to-frame in order to follow the “blob” moving through the scene over time, e.g., a p...

AI Technical Summary

Benefits of technology

Enables real-time detection and reporting of anomalous object interactions, improving the system's ability to identify unusual behavior without pre-defined patterns, enhancing surveillance capabilities.

Problems solved by technology

However, such surveillance systems typically are unable to identify or update objects, events, behaviors, or patterns (or classify such objects, events, behaviors, etc., as being normal or anomalous) by observing what happens in the scene over time; instead, such systems rely on static patterns defined in advance.

For example, such surveillance systems are unable to automatically distinguish anomalous object interactions (e.g., an unusual tailgating event) in a scene from ordinary object interactions (e.g., tailgating which occurs normally) in the scene and report instances of anomalous object interactions to a user.

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