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Markov random fields for abnormal behavior detection on highways

Conference Paper


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Abstract


  • This paper introduces a new paradigm for abnormal behavior detection relying on the integration of contextual information in Markov random fields. Contrary to traditional methods, the proposed technique models the local density of object feature vector, therefore leading to simple and elegant criterion for

    behavior classification. We develop a Gaussian Markov random field mixture catering for multi-modal density and integrating the neighborhood behavior into a local estimate. The convergence of the random field is ensured by online learning through a stochastic clustering algorithm. The system is tested on an extensive dataset (over 2800 vehicles) for behavior modeling. The experimental results show that abnormal behavior for a pedestrian walking, running and cycling on the highway, is detected with 82% accuracy at the 10% false alarm rate, and the system has an overall accuracy of 86% on the test data.

Publication Date


  • 2010

Citation


  • Bouttefroy, P., Beghdadi, A., Bouzerdoum, A. & Phung, S. (2010). Markov random fields for abnormal behavior detection on highways. European Workshop on Visual Information Processing (pp. 149-154). USA: IEEE.

Scopus Eid


  • 2-s2.0-79951590006

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=1824&context=infopapers

Ro Metadata Url


  • http://ro.uow.edu.au/infopapers/813

Has Global Citation Frequency


Start Page


  • 149

End Page


  • 154

Place Of Publication


  • USA

Abstract


  • This paper introduces a new paradigm for abnormal behavior detection relying on the integration of contextual information in Markov random fields. Contrary to traditional methods, the proposed technique models the local density of object feature vector, therefore leading to simple and elegant criterion for

    behavior classification. We develop a Gaussian Markov random field mixture catering for multi-modal density and integrating the neighborhood behavior into a local estimate. The convergence of the random field is ensured by online learning through a stochastic clustering algorithm. The system is tested on an extensive dataset (over 2800 vehicles) for behavior modeling. The experimental results show that abnormal behavior for a pedestrian walking, running and cycling on the highway, is detected with 82% accuracy at the 10% false alarm rate, and the system has an overall accuracy of 86% on the test data.

Publication Date


  • 2010

Citation


  • Bouttefroy, P., Beghdadi, A., Bouzerdoum, A. & Phung, S. (2010). Markov random fields for abnormal behavior detection on highways. European Workshop on Visual Information Processing (pp. 149-154). USA: IEEE.

Scopus Eid


  • 2-s2.0-79951590006

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=1824&context=infopapers

Ro Metadata Url


  • http://ro.uow.edu.au/infopapers/813

Has Global Citation Frequency


Start Page


  • 149

End Page


  • 154

Place Of Publication


  • USA