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SocialVAE: Human Trajectory Prediction Using Timewise Latents

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Computer Vision – ECCV 2022 (ECCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13664))

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Abstract

Predicting pedestrian movement is critical for human behavior analysis and also for safe and efficient human-agent interactions. However, despite significant advancements, it is still challenging for existing approaches to capture the uncertainty and multimodality of human navigation decision making. In this paper, we propose SocialVAE, a novel approach for human trajectory prediction. The core of SocialVAE is a timewise variational autoencoder architecture that exploits stochastic recurrent neural networks to perform prediction, combined with a social attention mechanism and a backward posterior approximation to allow for better extraction of pedestrian navigation strategies. We show that SocialVAE improves current state-of-the-art performance on several pedestrian trajectory prediction benchmarks, including the ETH/UCY benchmark, Stanford Drone Dataset, and SportVU NBA movement dataset.

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Acknowledgements

This work was supported by the National Science Foundation under Grant No. IIS-2047632.

Author information

Authors and Affiliations

  1. Clemson University, South Carolina, USA

    Pei Xu & Ioannis Karamouzas

  2. Roblox, San Mateo, USA

    Pei Xu

  3. CIMAT, A.C, Guanajuato, Mexico

    Jean-Bernard Hayet

Authors
  1. Pei Xu
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  2. Jean-Bernard Hayet
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  3. Ioannis Karamouzas
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Correspondence to Pei Xu .

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Editors and Affiliations

  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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Xu, P., Hayet, JB., Karamouzas, I. (2022). SocialVAE: Human Trajectory Prediction Using Timewise Latents. 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 13664. Springer, Cham. https://doi.org/10.1007/978-3-031-19772-7_30

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  • DOI: https://doi.org/10.1007/978-3-031-19772-7_30

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