关键词: 行为识别, 时空特征, 深度卷积神经网络, 深度学习, 行为矩阵

Abstract: In order to simultaneously calculate the temporal and spatial feature of behavior sequence samples, a convolution neural network recognition model with multi-scale convolution operator is proposed. Firstly, the skeleton vector in the sequence sample is integrated into a behavior matrix by superposition. Then the matrix is input into the recognition model. The convolution operators in each layers of the convolution neural network are extended to the multi-scale convolution operator in order to excavate the role of the bone points with different adjacency relations in describing human behavior, and the features obtained by this network are used to action identify. Experiments on MSR-Action3D dataset and HDM05 dataset achieve better recognition rate.

Key words: human action recognition, spatial-temporal feature, deep convolution network, deep learning, action matrix