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Learning Pedestrian Group Representations for Multi-modal Trajectory Prediction

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Computer Vision – ECCV 2022 (ECCV 2022)

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Abstract

Modeling the dynamics of people walking is a problem of long-standing interest in computer vision. Many previous works involving pedestrian trajectory prediction define a particular set of individual actions to implicitly model group actions. In this paper, we present a novel architecture named GP-Graph which has collective group representations for effective pedestrian trajectory prediction in crowded environments, and is compatible with all types of existing approaches. A key idea of GP-Graph is to model both individual-wise and group-wise relations as graph representations. To do this, GP-Graph first learns to assign each pedestrian into the most likely behavior group. Using this assignment information, GP-Graph then forms both intra- and inter-group interactions as graphs, accounting for human-human relations within a group and group-group relations, respectively. To be specific, for the intra-group interaction, we mask pedestrian graph edges out of an associated group. We also propose group pooling &unpooling operations to represent a group with multiple pedestrians as one graph node. Lastly, GP-Graph infers a probability map for socially-acceptable future trajectories from the integrated features of both group interactions. Moreover, we introduce a group-level latent vector sampling to ensure collective inferences over a set of possible future trajectories. Extensive experiments are conducted to validate the effectiveness of our architec ture, which demonstrates consistent performance improvements with publicly available benchmarks. Code is publicly available at https://github.com/inhwanbae/GPGraph.

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Acknowledgement

This work is in part supported by the Institute of Information & communications Technology Planning & Evaluation (IITP) (No. 2019-0-01842, Artificial Intelligence Graduate School Program (GIST), No. 2021-0-02068, Artificial Intelligence Innovation Hub), the National Research Foundation of Korea (NRF) (No. 2020R1C1C1012635) grant funded by the Korea government (MSIT), Vehicles AI Convergence Research & Development Program through the National IT Industry Promotion Agency of Korea (NIPA) funded by the Ministry of Science and ICT (No. S1602-20-1001), the GIST-MIT Collaboration grant and AI-based GIST Research Scientist Project funded by the GIST in 2022.

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Authors and Affiliations

  1. AI Graduate School, GIST, Gwangju, South Korea

    Inhwan Bae, Jin-Hwi Park & Hae-Gon Jeon

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  1. Inhwan Bae
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  2. Jin-Hwi Park
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  3. Hae-Gon Jeon
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Correspondence to Hae-Gon Jeon .

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  1. Tel Aviv University, Tel Aviv, Israel

    Shai Avidan

  2. University College London, London, UK

    Gabriel Brostow

  3. Google AI, Accra, Ghana

    Moustapha Cissé

  4. University of Catania, Catania, Italy

    Giovanni Maria Farinella

  5. Facebook (United States), Menlo Park, CA, USA

    Tal Hassner

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Bae, I., Park, JH., Jeon, HG. (2022). Learning Pedestrian Group Representations for Multi-modal Trajectory Prediction. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13682. Springer, Cham. https://doi.org/10.1007/978-3-031-20047-2_16

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