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Dynamic and Static Context-Aware LSTM for Multi-agent Motion Prediction

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Computer Vision – ECCV 2020 (ECCV 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12366))

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Abstract

Multi-agent motion prediction is challenging because it aims to foresee the future trajectories of multiple agents (e.g. pedestrians) simultaneously in a complicated scene. Existing work addressed this challenge by either learning social spatial interactions represented by the positions of a group of pedestrians, while ignoring their temporal coherence (i.e. dependencies between different long trajectories), or by understanding the complicated scene layout (e.g. scene segmentation) to ensure safe navigation. However, unlike previous work that isolated the spatial interaction, temporal coherence, and scene layout, this paper designs a new mechanism, i.e., Dynamic and Static Context-aware Motion Predictor (DSCMP), to integrates these rich information into the long-short-term-memory (LSTM). It has three appealing benefits. (1) DSCMP models the dynamic interactions between agents by learning both their spatial positions and temporal coherence, as well as understanding the contextual scene layout. (2) Different from previous LSTM models that predict motions by propagating hidden features frame by frame, limiting the capacity to learn correlations between long trajectories, we carefully design a differentiable queue mechanism in DSCMP, which is able to explicitly memorize and learn the correlations between long trajectories. (3) DSCMP captures the context of scene by inferring latent variable, which enables multimodal predictions with meaningful semantic scene layout. Extensive experiments show that DSCMP outperforms state-of-the-art methods by large margins, such as 9.05% and 7.62% relative improvements on the ETH-UCY and SDD datasets respectively.

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Acknowledgments

This work is partially supported by the SenseTime Donation for Research, HKU Seed Fund for Basic Research, Startup Fund and General Research Fund No.27208720. This work is also partially supported by the Major Project for New Generation of AI under Grant No. 2018AAA0100400 and by the National Natural Science Foundation of China under Grant No. 61772118.

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Authors and Affiliations

  1. The University of Hong Kong, Pokfulam, Hong Kong, China

    Chaofan Tao & Ping Luo

  2. University of Electronic Science and Technology of China, Chengdu, China

    Chaofan Tao & Lixin Duan

  3. SenseTime, Beijing, China

    Qinhong Jiang

Authors
  1. Chaofan Tao
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  2. Qinhong Jiang
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  3. Lixin Duan
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  4. Ping Luo
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Correspondence to Ping Luo .

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Editors and Affiliations

  1. University of Oxford, Oxford, UK

    Andrea Vedaldi

  2. Graz University of Technology, Graz, Austria

    Horst Bischof

  3. University of Freiburg, Freiburg im Breisgau, Germany

    Thomas Brox

  4. University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Jan-Michael Frahm

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Tao, C., Jiang, Q., Duan, L., Luo, P. (2020). Dynamic and Static Context-Aware LSTM for Multi-agent Motion Prediction. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12366. Springer, Cham. https://doi.org/10.1007/978-3-030-58589-1_33

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  • DOI: https://doi.org/10.1007/978-3-030-58589-1_33

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  • Print ISBN: 978-3-030-58588-4

  • Online ISBN: 978-3-030-58589-1

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