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Proceedings Paper

Surveillance video behaviour profiling and anomaly detection
Author(s): Chen Change Loy; Tao Xiang; Shaogang Gong
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Paper Abstract

This paper aims to address the problem of behavioural anomaly detection in surveillance videos. We propose a novel framework tailored towards global video behaviour anomaly detection in complex outdoor scenes involving multiple temporal processes caused by correlated behaviours of multiple objects. Specifically, given a complex wide-area scene that has been segmented automatically into semantic regions where behaviour patterns are represented as discrete local atomic events, we formulate a novel Cascade of Dynamic Bayesian Networks (CasDBNs) to model behaviours with complex temporal correlations by utilising combinatory evidences collected from local atomic events. Using a cascade configuration not only allows for accurate detection of video behaviour anomalies, more importantly, it also improves the robustness of the model in dealing with the inevitable presence of errors and noise in the behaviour representation resulting less false alarms. We evaluate the effectiveness of the proposed framework on a real world traffic scene. The results demonstrate that the framework is able to detect not only anomalies that are visually obvious, but also those that are ambiguous or supported only by very weak visual evidence, e.g. those that can be easily missed by a human observer.

Paper Details

Date Published: 25 September 2009
PDF: 11 pages
Proc. SPIE 7486, Optics and Photonics for Counterterrorism and Crime Fighting V, 74860E (25 September 2009); doi: 10.1117/12.832188
Show Author Affiliations
Chen Change Loy, Queen Mary, Univ. of London (United Kingdom)
Tao Xiang, Queen Mary, Univ. of London (United Kingdom)
Shaogang Gong, Queen Mary, Univ. of London (United Kingdom)

Published in SPIE Proceedings Vol. 7486:
Optics and Photonics for Counterterrorism and Crime Fighting V
Colin Lewis, Editor(s)

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