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We present a novel algorithm for detection of certain types of unusual events. The algorithm is based on multiple local monitors which collect low-level statistics. Each local monitor produces an alert if its current measurement is unusual, and these alerts are integrated to a final decision regarding the existence of an unusual event. Our algorithm satisfies a set of requirements that are critical for successful deployment of any large-scale surveillance system. In particular it requires a minimal setup (taking only a few minutes) and is fully automatic afterwards. Since it is not based on objects' tracks, it is robust and works well in crowded scenes where tracking-based algorithms are likely to fail. The algorithm is effective as soon as sufficeint low-level observations representing the routine activity have been collected, which usually happens after a few minutes. Our algorithm runs in realtime. It was tested on a variety of real-life crowded scenes. A ground-truth was extracted for these scenes, with respect to which detection and false-alarm rates are reported.
Video analysis, Computer vision
Amit Adam, Ehud Rivlin, Ilan Shimshoni, David Reinitz, "Robust Real-Time Unusual Event Detection using Multiple Fixed-Location Monitors", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 30, no. , pp. 555-560, March 2008, doi:10.1109/TPAMI.2007.70825
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