AN EFFICIENT ACTION RECOGNITION ALGORITHM BASED ON DEEP DYNAMIC FEATURE DUAL-STREAM CNN
In order to obtain the behavior information in video more efficiently,we propose a human action recognition method based on temporal convolutional neural network and dual-stream convolutional neural network.Multi-layer temporal convolution was used to obtain dynamic information from the video and obtain two-dimensional depth dynamic features.A dual-stream CNN was constructed,and depth dynamic features were used as input to the motion information stream instead of optical flow features.The dual-stream classification scores were fused in a weighted average to obtain a determination of the video action category.The algorithm was tested on public data set UCF101,HMDB51 and NTU-RGBD-60,with the highest accuracy of 94.2％,70.9％and 89.1％(cross-object experiments).When the accuracy is similar to the classical algorithms,such as ECO and TSM,the average parallel speed is increased by a factor of 2.1 and 3.6 respectively.The proposed algorithm improves the computational efficiency and is more practical.
万方数据
维普
