RETRACTED: Based on spatio-temporal graph convolution networks with residual connection for intelligence behavior recognition

Abstract

In the era of intelligent education, human behavior recognition based on computer vision is an important branch of pattern recognition. Human behavior recognition is a basic technology in the fields of intelligent monitoring and human-computer interaction in education. The dynamic changes of human skeleton provide important information for the recognition of educational behavior. Traditional methods usually use manual information to label or traverse rules only, resulting in limited representation capabilities and poor generalization performance of the model. In this paper, a kind of dynamic skeleton model with residual is adopted—a spatio-temporal graph convolutional network based on residual connections, which not only overcomes the limitations of previous methods, but also can learn the spatio-temporal model from the skeleton data. In the big bone NTU-RGB + D dataset, the network model not only improved the representation ability of human behavior characteristics, but also improved the generalization ability, and achieved better recognition effect than the existing model. In addition, this paper also compares the results of behavior recognition on subsets of different joint points, and finds that spatial structure division have better effects.

Traffic Flow Prediction with Attention Mechanism Based on TS-NAS

“…Based on spatial methods, a linear method [17] has been proposed and achieved good results. A method [18] has been proposed in action recognition and achieved good results. Based on septal methods, the general graph convolution neural network method is proposed [11], and the gating method is proposed [9], and this method is applied to traffic prediction.…”