Robust real-time unusual event detection using multiple fixed-location monitors.
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 sufficient 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.
Adam
Amit
A
Department of Computer Science, Technion Israel Institute of Technology, Haifa, Israel. amita@cs.technion.ac.il
Rivlin
Ehud
E
Shimshoni
Ilan
I
Reinitz
Daviv
D
D000465:Algorithms; D001185:Artificial Intelligence; D003199:Computer Systems; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D010781:Photography / Q000295:instrumentation / Q000379:methods*; D015203:Reproducibility of Results; D012637:Security Measures; D012680:Sensitivity and Specificity; D014741:Video Recording / Q000295:instrumentation / Q000379:methods*