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Parallel First-Order Markov Chain for On-Line Anomaly Detection in Traffic Video Surveillance

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5 Author(s)

This paper focuses on on-line anomaly detection in video traffic surveillance systems. Markov chain (MC) have been proposed already in computer and network intrusion detection. We applied them to the traffic domain and we propose to extend the classical MC (modeling all the behaviors in the scene) with an approach that evaluates in parallel a set of behavior specific MC. Such separate MCs are more discriminatory than a single MC for all the behaviors, allowing our approach to detect anomalies resulting from joining segments of normal behaviors. The learning of such models is done by using sequences of labeled normal behaviors and discretizing the image plane by using a simple grid. The approach has been validated on traffic surveillance videos, and experimental results show good performance both in terms of precision and recall

Published in:

Crime and Security, 2006. The Institution of Engineering and Technology Conference on

Date of Conference:

13-14 June 2006

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