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Adaptive Human Trajectory Prediction via Latent Corridors

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Computer Vision – ECCV 2024 (ECCV 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 15096))

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Abstract

Human trajectory prediction is typically posed as a zero-shot generalization problem: a predictor is learnt on a dataset of human motion in training scenes, and then deployed on unseen test scenes. While this paradigm has yielded tremendous progress, it fundamentally assumes that trends in human behavior within the deployment scene are constant over time. As such, current prediction models are unable to adapt to transient human behaviors, such as crowds temporarily gathering to see buskers, pedestrians hurrying through the rain and avoiding puddles, or a protest breaking out. We formalize the problem of context-specific adaptive trajectory prediction and propose a new adaptation approach inspired by prompt tuning called latent corridors. By augmenting the input of a pre-trained human trajectory predictor with learnable image prompts, the predictor improves in the deployment scene by inferring trends from extremely small amounts of new data (e.g., 2 humans observed for 30 s). With less than \(0.1\%\) additional model parameters, we see up to \(23.9\%\) ADE improvement in MOTSynth simulated data and \(16.4\%\) ADE in MOT and Wildtrack real pedestrian data. Qualitatively, we observe that latent corridors imbue predictors with an awareness of scene geometry and context-specific human behaviors that non-adaptive predictors struggle to capture.

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Acknowledgements

We thank Anastasios Angelopoulos, Antonio Loquercio, and Jathushan Rajasegaran for useful discussions and feedback. This work was supported by ONR MURI N00014-21-1-2801 and a NSF Graduate Fellowship.

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Authors and Affiliations

  1. UC Berkeley, Berkeley, USA

    Neerja Thakkar, Karttikeya Mangalam & Jitendra Malik

  2. Carnegie Mellon University, Pittsburgh, USA

    Andrea Bajcsy

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  1. Neerja Thakkar
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  2. Karttikeya Mangalam
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  3. Andrea Bajcsy
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  4. Jitendra Malik
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Correspondence to Neerja Thakkar .

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Editors and Affiliations

  1. University of Birmingham, Birmingham, UK

    Aleš Leonardis

  2. University of Trento, Trento, Italy

    Elisa Ricci

  3. Technical University of Darmstadt, Darmstadt, Hessen, Germany

    Stefan Roth

  4. Princeton University, Palo Alto, CA, USA

    Olga Russakovsky

  5. Czech Technical University in Prague, Prague, Czech Republic

    Torsten Sattler

  6. École des Ponts ParisTech, Marne-la-Vallée, France

    Gül Varol

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Thakkar, N., Mangalam, K., Bajcsy, A., Malik, J. (2025). Adaptive Human Trajectory Prediction via Latent Corridors. In: Leonardis, A., Ricci, E., Roth, S., Russakovsky, O., Sattler, T., Varol, G. (eds) Computer Vision – ECCV 2024. ECCV 2024. Lecture Notes in Computer Science, vol 15096. Springer, Cham. https://doi.org/10.1007/978-3-031-72920-1_17

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