关键词: 行为识别, 深度学习, 卷积神经网络, 长短时记忆网络（LSTM）, 上下文特征融合

Abstract: According to the human visual recognition mechanism, it is a feasible method to use a convolutional network to extract features to detect the appearance of an object and to detect motion using LSTM. This paper proposes a joint optimization architecture for convolutional long-term memory networks based on context feature fusion for behavior recognition networks with only RGB data. The 3D convolution kernel is used to extract the spatial features and short-term time features of the input action sequence, and the multi-channel information is fused, and the fused features are sent to the next-level convolutional neural network and the LSTM layer for long-term time characteristics learning, obtaining long-term spatial and temporal information of the context, and finally classifying human behavior using the Softmax classifier. The experimental results show that the average recognition accuracy of the convolutional long-term memory network based on context feature fusion is 93.62%, which is compared with the convolution length without feature fusion. The time-memory network is increased by 1.28% and the average detection time is reduced by 37.1%.

Key words: behavior recognition, deep learning, convolutional neural network, Long Short-Term Memory Network（LSTM）, contextual feature fusion