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Triple GNN: A Pedestrian-Scene-Object Joint Model for Pedestrian Trajectory Prediction

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Artificial Intelligence (CICAI 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13604))

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Abstract

Pedestrian trajectory prediction is an amazing but challenging task for vision guided applications, including autonomous driving, intelligent surveillance system, etc. Practically, the trajectory is a result of interaction among pedestrian’s surrounding people, scenes and related objects, which can be represented by a triple. In previous works, limited interactions have been exploited, such as pedestrian-pedestrian and pedestrian-object. These works are facing challenges when comprehensive interactions in natural scenes are involved. In this paper, we propose a triple graph neural network (Triple GNN) where interactions among pedestrians, scenes and objects are all taken into the prediction of pedestrian trajectory. Based on that, spatial relation is exploited to describe the mutual interaction among triple elements, and a two-stage optimization scheme is proposed on weights of the interaction aggregation for better relation exploitation and prediction. Furthermore, temporal relation is also exploited for compact representation and effective computation of future trajectories based on the spatial relation. Our method is verified via ETH and UCY datasets and achieves the state-of-the-art performance.

This work was supported in part by the National Key Research and Development Program of China under Grant 2020YFB2103501, in part by the National Natural Science Foundation of China under Grant 61971203.

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Authors and Affiliations

  1. School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, China

    Xinshengzi Huang, Qiong Liu & You Yang

Authors
  1. Xinshengzi Huang
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  2. Qiong Liu
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  3. You Yang
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Corresponding author

Correspondence to You Yang .

Editor information

Editors and Affiliations

  1. Tsinghua University, Beijing, China

    Lu Fang

  2. Xiaomi Inc., Beijing, China

    Daniel Povey

  3. Shanghai Jiao Tong University, Shanghai, China

    Guangtao Zhai

  4. JD Explore Academy, Beijing, China

    Tao Mei

  5. Chinese Academy of Sciences, Beijing, China

    Ruiping Wang

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Huang, X., Liu, Q., Yang, Y. (2022). Triple GNN: A Pedestrian-Scene-Object Joint Model for Pedestrian Trajectory Prediction. In: Fang, L., Povey, D., Zhai, G., Mei, T., Wang, R. (eds) Artificial Intelligence. CICAI 2022. Lecture Notes in Computer Science(), vol 13604. Springer, Cham. https://doi.org/10.1007/978-3-031-20497-5_6

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  • DOI: https://doi.org/10.1007/978-3-031-20497-5_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-20496-8

  • Online ISBN: 978-3-031-20497-5

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