3D Dual Path Networks and Multi-scale Feature Fusion for Human Motion Recognition
Weisong Che, Shuhua Peng · 2019
In this paper, a novel 3D dual path networks (3D-DPN) with 3D convolutional block attention module (3D-CBAM) and multi-scale feature fusion (MFF) was established. The architecture converted all 2D operations in the DPN structure into 3D and combined with 3D-CBAM to make the network have attention mechanisms for channel and spatiotemporal information. In order to keep the network sensitive to spatiotemporal input on different scales, we integrate the output of each 3D-DPN+CBAM block at the top of the network. This multi-scale feature fusion operation also allows the network to expand flexibly to accommodate different input frame lengths without significantly increasing the number of parameters and computation. We utilize this structure for human motion recognition based on the depth-sensing video. The experimental results indicate that our method higher-quality than the state-of-the-art model on multiple datasets.