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.
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