Local Feature Fusion Temporal Convolutional Network for Human Action Recognition

Aiming at the problem of action recognition of the three-dimensional human skeleton sequences, a temporal convolutional network(TCN) method combining local feature fusion is proposed. Firstly, the global spatial feature of the skeleton sequence is extracted by modeling all the spatial location changes of the skeleton sequence in an action. Then, according to the topological structure of human body joints and connection relations, the global spatial features are divided into local spatial features of the human body, and the obtained local spatial features are taken as the input of corresponding TCN to learn the internal feature relations of each joint. Finally, the feature vectors of each part of the output are fused to learn the cooperative relationship between the joints of each part, to complete the recognition of the action. Classification and recognition experiments are carried out on the most challenging data set NTU-RGB+D by the proposed method. The results show that compared with the existing methods based on CNN, LSTM and TCN, the classification accuracy of cross-subject and cross-view is improved to 79.5% and 84.6%, respectively.