Video Action Recognition Based on Spatio-temporal Feature Pyramid Module
Suming Gong, Ying Chen · 2020
Modeling the spatio-temporal information of different actions facilitates their recognition. The mainstream 2D convolutional network has low computational cost but cannot capture timing information; the mainstream 3D convolutional network can extract spatio-temporal features but has a huge amount of calculation and is difficult to deploy. In this paper, a Spatiotemporal Feature Pyramid Module(STFPM) is proposed to extract spatio-temporal feature information. STFPM captures temporal information between frames by dilated convolution and fuses feature information by weighted addition. STFPM can be flexibly inserted into the 2D backbone network in a plug-and-play manner. When equipped with STFPM, 2D ResNet-50 achieves good results on UCF101 dataset and HMDB51 dataset.