Physical Violence Detection in Video Streaming Using Partitioned Skeleton Analysis

We propose a skeleton-based method for identifying hostile behavior in this paper. The method does not require a lot of powerful hardware, but it is very quick to implement. Our approach consists of two stages: feature extraction from video frames to assess a person's posture, followed by action classification using a neural network to identify whether the frames include bullying situations. We also generated a dataset of 400 minutes of video data comprising one person's activities and 20 hours of video data including physical bullying and aggressive acts, as well as 13 classifications for distinguishing aggressor and victim behavior. On the gathered dataset, the method was put to the test. The results indicate that identifying aggressive conduct in video sequences is more than 97 percent accurate.