Dynamic Gesture Recognition Model Based on 3D Convolutional Neural Network

Abstract

As an important way of human-computer interaction,gesture interaction and gesture recognition have become current research hotspots in virtual reality,remote control and other fields due to their flexibility and convenience.Aiming at the problem that the accuracy of gesture recognition on the gesture video without the logo frame is affected,a dynamic gesture recognition method with a hierarchical network structure is proposed.The method uses the gesture detection model as the first level network,and gesture classification The model is a second-level network,which completes the identification task step by step.At the same time,in order to avoid the completion of the task in stages and the large number of parameters in the 3D convolutional neural network,resulting in excessive model training or running time consumption,a method of splitting the 3D convolution kernel into time domain convolution and spatial domain convolution is proposed.Method to reduce the time consumption of the model.The experimental results show that under the premise of ensuring real-time performance,the recognition accuracy rate on the experimental data set EgoGesture reaches 93.35%,which is better than C3D,ResNeXt101,MTUT and other methods,which proves the effectiveness of the method proposed in the article.