Abstract: Aiming at the problem that the joint adjacency graph of GCN in Spatial Temporal Graph Convolutional  Network (ST-GCN) is not easy to learn the semantic information between distal joints and that TCN is insufficient in describing time information, the digital signature preprocessing is introduced to enhance data, and a signature spatial improved temporal graph convolutional network (SSIT-GCN) is proposed. Firstly, the time series of human joint locations are input into the signature layer to preprocess the data, and they are transformed into a multi-dimensional path by various embedding algorithms. The multi-dimensional path  is divided into multiple paths and the signature features of each path are calculated. Secondly, the adjacency matrix of GCN is redesigned, and deconvolution is used to replace zero padding to keep the size of TCN unchanged, and a 1×1 convolution kernel is also introduced to increase the nonlinearity to improve ST-GCN, so as to obtain spatial improved temporal graph convolutional network(SIT-GCN). Finally, the original data is replaced by signature features that is input into SIT-GCN to obtain the final result. The experimental results show that the signature-based SSIT-GCN greatly improves the training accuracy, reduces the training time, and has better recognition ability and speed for dynamic gesture recognition.

Key words: gesture recognition, path signature, spatial temporal graph convolution network(ST-GCN), supervised learning, signature layer ,