Towards Abnormal Trajectory and Event Detection in Video Surveillance

Serhan Cosar 1 Giuseppe Donatiello 2 Vania Bogorny 3 Carolina Garate 3 Luis Alvares 4 François Bremond 3
1 University of Lincoln
2 Technogym spa
3 STARS - Spatio-Temporal Activity Recognition Systems
CRISAM - Inria Sophia Antipolis - Méditerranée
4 UFSC - Universidade Federal de Santa Catarina [Florianópolis]
Abstract : In this paper we present a unified approach for abnormal behavior detection and group behavior analysis in video scenes. Existing approaches for abnormal behavior detection do either use trajectory based or pixel based methods. Unlike these approaches, we propose an integrated pipeline that incorporates the output of object trajectory analysis and pixel-based analysis for abnormal behavior inference. This enables to detect abnormal behaviors related to speed and direction of object trajectories, as well as complex behaviors related to finer motion of each object. By applying our approach on three different datasets, we show that our approach is able to detect several types of abnormal group behaviors with less number of false alarms compared to existing approaches.
Keywords : trajectory-based analysis group behavior analysis abnormal event detection fused approach pixel-based analysis
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Journal articles
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https://hal.inria.fr/hal-01849787
Contributor : Soumik Mallick <>
Submitted on : Thursday, July 26, 2018 - 2:45:43 PM
Last modification on : Wednesday, October 10, 2018 - 10:09:53 AM
Long-term archiving on : Saturday, October 27, 2018 - 1:55:38 PM

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Serhan Cosar, Giuseppe Donatiello, Vania Bogorny, Carolina Garate, Luis Alvares, et al.. Towards Abnormal Trajectory and Event Detection in Video Surveillance. IEEE Transactions on Circuits and Systems for Video Technology, Institute of Electrical and Electronics Engineers, 2016. ⟨hal-01849787⟩

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