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.

Archetti, F., Manfredotti, C., Matteucci, M., Messina, V., Sorrenti, D. (2006). Parallel First-Order Markov Chain for On-Line Anomaly Detection in Traffic Video Surveillance. In Imaging for Crime Detection and Prevention - ICDP 2006 (pp.582-587). London : IET (Institution of Engineering and Technology).

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

ARCHETTI, FRANCESCO ANTONIO;MANFREDOTTI, CRISTINA ELENA;MESSINA, VINCENZINA;SORRENTI, DOMENICO GIORGIO
2006

Abstract

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.
slide + paper
parallel, order, markov, chain, line, anomaly, detection, traffic, video, surveillance
English
Imaging for Crime Detection and Prevention - ICDP 2006
0-86341-647-0
Archetti, F., Manfredotti, C., Matteucci, M., Messina, V., Sorrenti, D. (2006). Parallel First-Order Markov Chain for On-Line Anomaly Detection in Traffic Video Surveillance. In Imaging for Crime Detection and Prevention - ICDP 2006 (pp.582-587). London : IET (Institution of Engineering and Technology).
Archetti, F; Manfredotti, C; Matteucci, M; Messina, V; Sorrenti, D
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/15198
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