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Histograms of optical flow orientation for abnormal events detection

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Abstract

In this paper, we propose an algorithm to detect abnormal events based on video streams. The algorithm is based on histograms of the orientation of optical flow descriptor and one-class SVM classifier. We introduce grids of Histograms of the Orientation of Optical Flow (HOF) as the descriptors for motion information of the monolithic video frame. The one-class SVM, after a learning period characterizing normal behaviors, detects the abnormality which is considered as the event needed to be recognized in the current frame. Extensive testing on dataset corroborates the effectiveness of the proposed detection method.
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hal-02319093 , version 1 (17-10-2019)

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Tian Wang, Hichem Snoussi. Histograms of optical flow orientation for abnormal events detection. 2013 IEEE International Workshop on Performance Evaluation of Tracking and Surveillance (PETS 2013), Jan 2013, Clearwater, United States. pp.45-52, ⟨10.1109/PETS.2013.6523794⟩. ⟨hal-02319093⟩
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