Dynamic Gesture Recognition Combining Two-stream 3D Convolution with Attention Mechanisms

Abstract

Benefits from the progress of computer hardware and computing power, natural and simple dynamic gesture recognition gets a lot of attention in human-computer interaction. In view of the requirement of the accuracy of dynamic gesture recognition in human-computer interaction, a method of dynamic gesture recognition that combines Two-stream Inflated 3D (I3D) Convolution Neural Network (CNN) with the Convolutional Block Attention Module (CBAM-I3D) is proposed. In addition, relevant parameters and structures of the I3D network model are improved. In order to improve the convergence speed and stability of the model, the Batch Normalization (BN) technology is used to optimize the network, which shortens the training time of the optimized network. At the same time, experimental comparisons with various Two-stream 3D convolution methods on the open source Chinese Sign Language (CSL) recognition dataset are performed. The experimental results show that the proposed method can recognize dynamic gestures well, and the recognition rate reaches 90.76%, which is higher than other dynamic gesture recognition methods. The validity and feasibility of the proposed method are verified.