This paper applies a video modelling technique to a surveillance scenario where pedestrians are monitored to detect unusual events. The aim is to investigate the components of an automatic vision system capable of detecting normal and abnormal behaviour. Such a system has application in surveillance scenarios like town centre plazas, stadiums, train stations and shopping malls. Surveillance usually relies on tracking, but in crowded scenarios tracking is not reliable. Thus our framework for representation and analysis is based on optical flow to avoid tracking of individuals. We demonstrate that patterns derived from optical flow and encoded by a Hidden Markov Model are able to capture the dynamic evolution of normal behaviour allowing the classification of abnormal events.
Characterisation of optical flow anomalies in pedestrian traffic
2005-01-01
6 pages
Aufsatz (Zeitschrift)
Englisch
video signal processing , computer vision , gesture recognition , hidden Markov model , hidden Markov models , unusual event detection , normal behaviour detection , optical flow anomaly characterization , behavioural sciences computing , video modelling technique , automatic vision system , abnormal behaviour detection , road traffic , pedestrian monitoring , feature extraction , surveillance , dynamic evolution , pedestrian traffic , image sequences
First-Order Pedestrian Traffic Flow Theory
Transportation Research Record | 2005
|First-Order Pedestrian Traffic Flow Theory
British Library Conference Proceedings | 2005
|Engineering Index Backfile | 1924
|First-Order Pedestrian Traffic Flow Theory
British Library Conference Proceedings | 2005
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