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International Journal of Machine Learning and Cybernetics

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04-04-2022 | Original Article

CIRAN: extracting crowd interaction with residual attention network for pedestrian trajectory prediction

Authors: Shang Liu, Xiaoyu Chen, Hao Chen

Published in: International Journal of Machine Learning and Cybernetics | Issue 9/2022

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Abstract

This paper proposes a new deep learning network based on the spatial attention mechanism—crowd interaction with residual attention network (CIRAN), which combines the position and velocity information of neighbor pedestrians for trajectory prediction. It adaptively selects the most effective areas of the scene by using the residual attention module to obtain more accurate and reasonable pedestrian trajectories. Therefore, the accuracy of prediction can be improved. In addition, the velocity encoding module is introduced to transform the coordinate based pedestrian social interaction process into the spatial grid based pedestrian social interaction process. Based on two public data, ETH and UCY, this paper obtains the most advanced experimental results up to now, and these results show the validity of the proposed CIRAN.

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Title: CIRAN: extracting crowd interaction with residual attention network for pedestrian trajectory prediction
Authors: Shang Liu
Xiaoyu Chen
Hao Chen
Publication date: 04-04-2022
Publisher: Springer Berlin Heidelberg
Published in: International Journal of Machine Learning and Cybernetics / Issue 9/2022
Print ISSN: 1868-8071
Electronic ISSN: 1868-808X
DOI: https://doi.org/10.1007/s13042-022-01548-0

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