Anomaly Detection for Video Surveillance Applications

Authors:
Carmen E. Au;Sandra Skaff;James J. Clark
Affiliations:
McGill University, Montreal, Canada;McGill University, Montreal, Canada;McGill University, Montreal, Canada
Venue:
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
Year:
2006

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Abstract

We investigate the problem of anomaly detection for video surveillance applications. In our approach, we use a compression-based similarity measure to determine similarity between images in a video sequence. Images that are sufficiently dissimilar are deemed anomalous and stored to be compared against subsequent images in the sequence. The goal of our research is two-fold; in addition to detecting anomalous images, the issue of heavy computational and storage resource demands is addressed.