Violence detection in videos by combining 3D convolutional neural networks and support vector machines

ideo-surveillance has always been a vital tool to enforce safety in both public and private environments. Even though (smart) cameras are nowadays relatively widespread and cheap, such monitoring systems lack effectiveness in most scenarios. In addition, there is no guarantee about a human operator who monitors rare events in live video footages, forcing the use of such systems after unwanted events already took their undisturbed course, as a mere tool for investigations. Having an intelligent software to perform the task would allow to unlock the full potential of video-surveillance systems. To this end, in this paper we propose a solution based on a 3D Convolutional Neural Network that can effectively detect fights, aggressive motions and violence scenes in live video streams. Compared to state-of-the-art techniques, our method showed very promising performance on three challenging benchmark datasets: Hockey Fight, Crowd Violence and Movie Violence.