Pedestrian trajectory prediction algorithm based on graph convolutional network

To solve the problem that the pedestrian interaction model is difficult to be effectively constructed in the pedestrian trajectory prediction task, a trajectory prediction algorithm based on graph convolutional network (TP-GCN) was proposed to establish pedestrian interaction and predict future trajectories of pedestrians. First, the long short-term memory was used to extract the trajectory motion features of the trajectory sequences of pedestrians. Then, the pedestrians were considered as the nodes on the graph, and adjacency matrix was built to represent the created interactions. Next, the connection weights between unrelated nodes were screened out according to the blind zone. For trajectory motion features, the graph convolutional network was applied to extract the interactions between the trajectories and increase the extraction of the interaction in each trajectory, and the interaction was then encoded as trajectory interaction features by using long short-term memory. Furthermore, the weights of the graph convolutional network were optimized by the Deep Graph Info method to ensure that the motion pattern of individual accords with those of all the pedestrians in the scene. Finally, the trajectory motion features and trajectory interaction features were decoded using long short-term memory to complete the trajectory prediction. According to the experiment on the public datasets ETH and UCY, the proposed algorithm could make the predictions of pedestrian habits close to the real trajectories based on the interaction model between pedestrians, and the overall prediction accuracy was high. In addition, the ablation experiment and the visualization of the predicted trajectory also verified the effectiveness and interpretability of the algorithm. Copyright ©2021 Journal of Harbin Institute of Technology.All rights reserved.