TSTSC: temporal-spatial transformers with self-consistency constraints in pedestrian trajectory prediction

Mingxu Wang; Xinhua Zeng; Haiming Peng; Weilong Lin; Chengxin Pang

doi:10.1117/12.3034085

13 June 2024 TSTSC: temporal-spatial transformers with self-consistency constraints in pedestrian trajectory prediction

Mingxu Wang, Xinhua Zeng, Haiming Peng, Weilong Lin, Chengxin Pang

Proceedings Volume 13180, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024); 1318067 (2024) https://doi.org/10.1117/12.3034085
Event: International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024), 2024, Guangzhou, China

Abstract

For the domains of path planning, environment perception, and control in autonomous driving, pedestrian trajectory prediction is highly significant. Given the uncertainty and complexity of potential movement directions in crowded scenarios, coupled with strong spatial interactions and temporal dependencies, accurately predicting pedestrian trajectories presents a challenging task. However, previous works have often overlooked the consideration of temporal consistency, which could introduce disturbances and interfere with the accuracy of prediction results. In our proposed approach, we construct a framework based on both temporal and spatial Transformers (referred to as TSTSC) to model pedestrian spatial graphs and capture temporal-spatio interactions. Specifically, we introduce a self-consistency constraint module based on self-supervised learning to ensure temporal consistency within scene intervals, reducing the impact of disturbances. This study tests using popular public datasets (ETH and UCY) and compares against existing methods. Experimental results demonstrate a significant improvement in predictive accuracy compared to baseline models, validating the soundness of our hypothesis.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Mingxu Wang, Xinhua Zeng, Haiming Peng, Weilong Lin, and Chengxin Pang "TSTSC: temporal-spatial transformers with self-consistency constraints in pedestrian trajectory prediction", Proc. SPIE 13180, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024), 1318067 (13 June 2024); https://doi.org/10.1117/12.3034085

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KEYWORDS

Transformers

Data modeling

Neural networks

Education and training

Machine learning

Image processing

Modeling

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