Spatio-temporal feature using optical flow based distribution for violence detection

Violence detection in video surveillance is a challenging task in computer vision. In this paper, we propose a new feature that describes violence in video based on interest points, detected in both space and time domains, and optical flow information. The proposed feature estimates the bivariate distribution of the optical flow magnitude and orientation computed around spatio-temporal interest points (STIP). The estimation of the distribution is performed using the bivariate kernel density estimation method. Our proposed descriptor, named Distribution of Magnitude and Orientation of Local Interest Frame (DiMOLIF), is used to learn a SVM based binary classifier. Experiments are conducted on two well-known benchmark datasets for violence detection in both crowded and uncrowded scenes. Obtained results show that our proposed feature is efficient and outperforms the state of the art descriptors.