In the process of pedestrian movement, the trajectory is not only related to their subjective intention, but also affected by the surrounding agents and the environment. How to more effectively model the interaction between agents plays a very significant role in trajectory prediction task, which is also the focus of researchers’ work. To solve this problem, this paper proposes a knowledge graph construction method based on trajectory clustering to extract the interactive features between adjacent pedestrians. Based on the fact that the behavior of pedestrians has the property of group psychology, we first do spectral clustering on the trajectory of pedestrians to find their inter class information. Then, through the analysis of the clustering results and the movement angle of pedestrians, the interactive knowledge graph structure of each frame is constructed. Finally, we fuse it with the relative distance graph of pedestrians to encode the interactively social relation in trajectory prediction. Through the evaluation on the public ETH and UCY datasets, the superiority of our method is demonstrated by exhaustive experiments.
Learning Interactive Knowledge Graph for Trajectory Prediction
Lect. Notes Electrical Eng.
International Conference on Autonomous Unmanned Systems ; 2021 ; Changsha, China September 24, 2021 - September 26, 2021
Proceedings of 2021 International Conference on Autonomous Unmanned Systems (ICAUS 2021) ; Kapitel : 127 ; 1269-1279
18.03.2022
11 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
Learning Interactive Knowledge Graph for Trajectory Prediction
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