A Classification Method Based on Optical Flow for Violence Detection

Violence detection is one of the substantial and challenging topics in intelligent video surveillance systems. As there is a growing demand on video surveillance systems with the capability of automatic violence detection, we focus on existing violence detection methods to improve them. In this paper, we introduce a new feature descriptor named Histogram of Optical flow Magnitude and Orientation (HOMO). First, the proposed method converts input frames to the grayscale format. Next, it computes the optical flow between two consequence frames. Then, the optical flow magnitude and orientation of each pixel in each frame are compared separately with its predecessor frame to obtain meaningful changes of magnitude and orientation. Subsequently, different threshold values are applied to the magnitude and orientation changes for obtaining six binary indicators. Finally, these binary indicators are analyzed to get the HOMO descriptor which is used to train a SVM classifier. The system has been implemented using MATLAB. To evaluate the proposed method, two benchmark datasets have been used. The comparison of HOMO and other descriptors on benchmark datasets demonstrates satisfactory performance. (C) 2019 Elsevier Ltd. All rights reserved.