一种融合全局时空特征的CNNs动作识别方法

The existing human motion recognition methods based on convolution neural network (CNNs) usually use spatial or temporal local features. In this paper, a two-stream CNNs action recognition model was proposed, which integrated the global temporal and spatial features of human action. Motion images were deeply studied in spatial channels, the multi frame fusion way was used to raise the accuracy rate, and deep learning on the energy motion history image (EMHI) was performed in the global temporal stream. Finally, the two streams were combined to identify the human motion. In order to solve the problem of insufficient training samples in the learning process, the existing large data sets was used for pre-training. Experiments were carried out on the UCF101 dataset and the small sample dataset of the project. The results demonstrate the effectiveness of the method.