Behavior Recognition Based on Two-Stream Temporal Relation-Time Pyramid Pooling Network (TTR-TPPN)

Nowadays, intelligent surveillance has received extensive attention from academia, business, and industry. Deep learning algorithms are widely used in the field of intelligent surveillance. Recently, most deep learning models are limited to a short-term behavior recognition in the entire video. In order to better identify human behavior in the video, we combined a Two-stream network and a Temporal Relation network (TRN) and added a time pyramid pooling operation. In this way, the Two-Stream Temporal Relation-Time Pyramid Pooling Network (TTR-TPPN) can be constructed. The relational pyramid pool network integrated the frame-level features in the video into video-level features. We applied the TTR-TPPN to the Internet public standard data set UCF101 and the self-made DW20 data set. It is found through experiments that this network has a higher recognition rate than other behavior recognition methods on both data sets, and it has better performance in long-term behavior recognition. Therefore, the TTR-TPPN enables it to recognize long-time sequence behavior and improves the accuracy of human behavior recognition.