Fight detection in surveillance videos
Ersin Esen, Mehmet Ali Arabacı, Medeni Soysal · 2013
Fight detection is an important topic for surveillance systems. However, there has been little success in creating an algorithm that can detect fight in surveillance videos with high performance. In this work, we propose a new method for the task of fight detection in surveillance videos. The proposed method relies on a novel motion feature, namely Motion Co-Occurrence Feature (MCF). Firstly, motion vectors are extracted by using block matching algorithm. Secondly, direction and magnitude values of motion vectors are quantized separately. Afterwards, direction and magnitude based MCF is calculated by considering both current and past motion vectors. Experimental results obtained using k-Nearest Neighbor classifier showed that the proposed algorithm can discriminate fight scenes with significantly high accuracy.