Trajectory-Pooled Deep Convolutional Networks for Violence Detection in Videos

Violence detection in videos is of great importance in many applications, ranging from teenagers protection to online media filtering and searching to surveillance systems. Typical methods mostly rely on hand-crafted features, which may lack enough discriminative capacity for the specific task of violent action recognition. Inspired by the good performance of deep models for human action recognition, we propose a novel method for detecting human violent behaviour in videos by integrating trajectory and deep convolutional neural networks, which takes advantage of hand-crafted features [21] and deep-learned features [23]. To evaluate this method, we carry out experiments on two different violence datasets: Hockey Fights dataset and Crowd Violence dataset. The results demonstrate the advantage of our method over state-of-the art methods on these datasets.