Global temporal pyramid for human abnormal action recognition

With the development of monitoring technology and the improvement of people's security awareness, intelligent human abnormal action recognition technology in the field of action recognition is increasingly high. In most cases, abnormal human action may have little difference in appearance compared with normal behavior, so the control of visual rhythm information becomes an important factor affecting action recognition, but people often focus on the appearance information of the action and ignore the rhythm information. In this paper, we introduce the temporal pyramid module to process the visual tempos information, meanwhile, the traditional LSTM local history information transfer method is very easy to lose the context information, which is not conducive to the grasp of global information and thus will greatly affect the processing effect of the temporal pyramid. This paper introduces a non-local neural network module to enhance the network's ability to grasp global information and the model's long-range modeling capability, which is used to supplement the temporal pyramid module. Finally, this paper uses the mainstream anomaly dataset UCF-Crime to test the network performance, and the improved network model recognition accuracy AUC reaches 0.82, which is better than other stateof-the-art methods.