Fusion of Skeleton and RGB Features for RGB-D Human Action Recognition

The output of Microsoft Kinect is a multimodal signal, which provides RGB videos, depth sequences and skeleton information at the same time, opening up a new opportunity for the research of human action recognition. However, for different single modalities of the signals, how to exploit and fuse useful features of these various sources remains a very challenging problem. Most of the methods based on RGB-D action recognition simply fuse the multimodal features, ignoring the potential semantic relationship between different models. In this paper, we propose a multi-modal action recognition model based on Bilinear Pooling and Attention Network (BPAN), which could effectively fuse multi-modal for RGB-D action recognition. Firstly, we adopt the efficient data preprocessing methods for RGB and skeleton data. Then, we propose a multimodal fusion network combining RGB video and skeleton sequences. The proposed BPAN module could effectively compress the features of RGB and skeleton, and project them into latent subspace to get the fusion features. In the end, a fully connected three-layer perceptron is adopted to obtain the final classification decision. Experimental results on three public datasets demonstrate that our proposed method leads to a more favorable performance compared with the state-of-the-art methods.