关键词: 行为识别, 3D卷积神经网络, 残差网络, 双数据流, 深度学习理论

Abstract: In view of the problems of too large video stream data and too many setting 3D convolution kernel parameters in human behavior based on video,which led to long training time and difficulty in tuning the parameters,a method to divide 3D convolution kernel into two kinds of convolution kernels that were space domain and time domain was proposed based on 3D convolutional neural network.Two data streams formed by two convolution kernels were interact with each other,thus the network structure and reducing parameter settings were optimizated.The training verification was performed on two behavioral identification datasets named KTH and UCF101 and the accuracy rate of recognition behavior was 96.2% and 90.7% respectively.The results showed that the proposed method could speed up the training progress by 7.5%~7.8% and ensure the training accuracy at the same time.Therefore,this method could effectively reduce the hardware requirements for deep learning in behavior recognition and improve the efficiency of model training,which could be widely used in the field of intelligent robots.

Key words: behavior recognition, 3D convolutional neural network, residual network, dual flow data, deep learning theory