International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD2023)

In the field of computer vision, human motion prediction is a classic problem with many great uses. Methods for predicting human movement aim to process and analyze human movement data for predicting future human movement. There are complex problems of loss constraint and training process in this method. For solving the above problems, reduce the training and prediction time and provide a new perspective, this paper uses a lightweight and efficient graph convolutional network combined with multi-layer perceptron model, including a fully connected layer graph convolutional network. What’s more, for further improving the prediction accuracy, we also compare some paper models with this model and find that this model has better accuracy performance. Finally, experiments on Human3.6M dataset verify the effectiveness of the proposed method, the proposed method can accurately predict the future human behavior, the accuracy can reach 90.2%.In the field of computer vision, human motion prediction is a classic problem with many great uses. Methods for predicting human movement aim to process and analyze human movement data for predicting future human movement. There are complex problems of loss constraint and training process in this method. For solving the above problems, reduce the training and prediction time and provide a new perspective, this paper uses a lightweight and efficient graph convolutional network combined with multi-layer perceptron model, including a fully connected layer graph convolutional network. What’s more, for further improving the prediction accuracy, we also compare some paper models with this model and find that this model has better accuracy performance. Finally, experiments on Human3.6M dataset verify the effectiveness of the proposed method, the proposed method can accurately predict the future human behavior, the accuracy can reach 90.2%.