关键词: 动作识别, 稀疏编码, 简化粒子群, 深度序列, 局部时空描述子

Abstract: To overcome the slow training speed and low recognition rate of the existing action recognition algorithm, an action recognition method based on the sparse coding local spatio-temporal descriptor is proposed. The method firstly extracts the normal of the depth image, and uses the adaptive spatio-temporal pyramid based on the action energy to block the action frames. Then the local spatio-temporal descriptors are obtained by the local aggregation normal. The local spatio-temporal descriptors are encoded by sparse coding to get a set of dictionary vectors to reconstruct the sample data. Finally, the simplified Particle Swarm Optimization（sPSO） is used to optimize the SVM classifier to find the most suitable sample data classification model. The experiment achieves a recognition rate of 93.80% and 95.83% on MSRAction3D and MSRGesture3D datasets, and the training speed is significantly improved compared with the previous methods, which proves the effectiveness and robustness of the method.

Key words: action recognition, sparse coding, simplified Particle Swarm Optimization（sPSO）, depth sequence, local spatio-temporal descriptors