Detection of Abnormal Visual Events via Global Optical Flow Orientation Histogram - Archive ouverte HAL Access content directly
Journal Articles IEEE Transactions on Information Forensics and Security Year : 2014

Detection of Abnormal Visual Events via Global Optical Flow Orientation Histogram

(1) , (1)
1

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.
Not file

Dates and versions

hal-02307896 , version 1 (08-10-2019)

Identifiers

Cite

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

Altmetric

Share

Gmail Facebook Twitter LinkedIn More