Two-Level Attention Model Based Video Action Recognition Network

The complex environment background, lighting conditions, and other action-irrelevant visual information in the video frame bring a lot of redundancy and noise to the action spatial features, which seriously affects the accuracy of action recognition. Aiming at this point, we propose a recurrent region attention cell to capture the action-relevant regional visual information in the spatial feature, and according to the temporal sequential natures of the video, on the basis of the recurrent region attention cell, a Recurrent Region Attention model (RRA) is proposed. The recurrent region attention cell in the RRA iterates according to the temporal sequence of the video, so that the attention performance of the RRA is gradually improved. Secondly, we propose a Video Frame Attention model (VFA) that can highlight the more important frames in the whole action video sequence, so as to reduce the interference caused by the similarity between the heterogeneous action video sequences. Finally, we propose an end-to-end trainable network: Two-level Attention Model based video action recognition network (TAMNet). We experimented on two video action recognition benchmark datasets: UCF101 and HMDB51. Experiments show that our end-to-end TAMNet network can reliably focus on the more important video frames in the video sequence, and effectively capture the action-relevant regional visual information in the spatial features of each frame of the video sequence. Inspired by the two-stream structure, we construct a two-modalities TAMNet network. In the same training conditions, the two-modalities TAMNet network achieved optimal performance on both datasets.