Detection of Abnormal Visual Events via Global Optical Flow Orientation Histogram

Abstract : The aim of this paper is to detect abnormal events in video streams, a challenging but important subject in video surveillance. We propose a novel algorithm to address this problem. The algorithm is based on an image descriptor and a nonlinear classification method. We introduce a histogram of optical flow orientation as a descriptor encoding the moving information of each video frame. The nonlinear one-class support vector machine classification algorithm, following a learning period characterizing the normal behavior of training frames, detects abnormal events in the current frame. Further, a fast version of the detection algorithm is designed by fusing the optical flow computation with a background subtraction step. We finally apply the method to detect abnormal events on several benchmark data sets, and show promising results.
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https://hal-utt.archives-ouvertes.fr/hal-02307896
Contributor : Jean-Baptiste Vu Van <>
Submitted on : Tuesday, October 8, 2019 - 9:46:59 AM
Last modification on : Wednesday, October 9, 2019 - 1:37:45 AM

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Tian Wang, Hichem Snoussi. Detection of Abnormal Visual Events via Global Optical Flow Orientation Histogram. IEEE Transactions on Information Forensics and Security, Institute of Electrical and Electronics Engineers, 2014, 9 (6), pp.988-998. ⟨10.1109/TIFS.2014.2315971⟩. ⟨hal-02307896⟩

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