A Skeleton-based Approach for Campus Violence Detection

In this paper, we propose a skeleton-based method to identify violence and aggressive behavior. The approach does not necessitate high-processing equipment and it can be quickly implemented. Our approach consists of two phases: feature extraction from image sequences to assess a human posture, followed by activity classification applying a neural network to identify whether the frames include aggressive situations and violence. A video violence dataset of 400 min comprising a single person's activities and 20 h of video data including physical violence and aggressive acts, and 13 classifications for distinguishing aggressor and victim behavior were generated. Finally, the proposed method was trained and tested using the collected dataset. The results indicate the accuracy of 97% was achieved in identifying aggressive conduct in video sequences. Furthermore, the obtained results show that the proposed method can detect aggressive behavior and violence in a short period of time and is accessible for real-world applications.