CAMPUS FIGHTING BEHAVIOR DETECTION BASED ON SKELETON SEQUENCES

In the field of campus security, the identification of violent behaviors currently mainly relies on manual labor, which is prone to omissions. Skeleton-based spatio-temporal graph convolutional network (ST-GCN) has high behavior recognition accuracy, but it is mainly used for single person recognition. This paper proposes a method of identifying violence against campus surveillance video, which adds a multi-target tracking module on the basis of ST-GCN. The OpenPose algorithm was used to obtain the human skeleton set in the video frame, and the single-person skeleton sequence was separated by the Markov chain Monte Carlo data association method and input into ST-GCN for violent behavior recognition. The experimental results on the data set RWF-2000 show that the recognition rate of this method reaches 87.75%, which is higher than other existing models.