Behavior recognition based on enhanced human pose estimation

Abstract

In public places, rapid and accurate behavior recognition helps to monitor the occurrence of dangerous events. With the rise of neural networks, many methods have been developed to handle behavior recognition problems. Unlike target recognition, behavior recognition requires not only analyzing the spatial dependencies of the target, but also analyzing the historical information of target changes. We have inherited the traditional dual stream recognition method, in order to better extract spatial and temporal features in consistency, strengthen the contextual information extraction ability and channel fusion. In this paper, we propose a parallel convolutional network (MFC network) for extracting richer contextual information from dual flow branches. In addition, we propose a spatiotemporal modal interaction module (ST block) to improve the robustness of the network. The experimental results on datasets HMDB-51 and UFC-101 show that the method is effective.