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Lightweight graph convolutional network with fusion data for skeleton based action recognition

Action recognition methods based on human skeletons can clearly express human actions. We present a lightweight graph convolutional network with various streams of data, given the network’s computational complexity and high computational cost of current mainstream human action recognition networks. First, four characteristic data streams are fused using a multi-stream data fusion algorithm, and the best result can be produced with only one training session, minimizing the network’s computational complexity. Second, a non-local graph convolution module based on the graph convolutional network is designed to collect the image’s global information and increase action recognition accuracy. Finally, the spatial Ghost graph convolution module and the temporal Ghost graph convolution module are intended to minimize the network’s computational complexity even more. On the action recognition datasets NTU60 RGB+D and NTU120 RGB+D dataset Our methods achieve highly competitive performance, with average precision of 96.4 and 87.5 percent respectively.