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

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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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Acknowledgements

This work is supported by the Natural Science Foundation of Tianjin City (Grants 18JCYBJC85100), Humanities and Social Science Fund of Ministry of Education of China (Grant 19YJA630046) and Scientific Research Plan Project of Tianjin Municipal Education Commission (Grant No. 2017KJ237).

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Authors and Affiliations

  1. College of Science and Technology, Tianjin University of Finance and Economics, Tianjin, China

    Shang Liu, Xiaoyu Chen & Hao Chen

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  1. Shang Liu
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  2. Xiaoyu Chen
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  3. Hao Chen
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Correspondence to Shang Liu.

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Liu, S., Chen, X. & Chen, H. CIRAN: extracting crowd interaction with residual attention network for pedestrian trajectory prediction. Int. J. Mach. Learn. & Cyber. 13, 2649–2662 (2022). https://doi.org/10.1007/s13042-022-01548-0

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