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Pedestrian Trajectory Prediction Using a Social Pyramid

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PRICAI 2019: Trends in Artificial Intelligence (PRICAI 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11671))

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Abstract

Understanding and forecasting human movement paths are vital for a wide range of real world applications. It is not an easy task to generate plausible future paths as the scenes and human movement patterns are often very complex. In this paper, we propose a social pyramid based prediction method (SPP), which includes two encoders to capture motion and social information. Specifically, we design a social pyramid map structure for the Social encoder, which can differentiate the influence of other pedestrians in nearby areas or remote areas based on their spatial locations. For the Motion encoder, a mixing attention mechanism is proposed to combine the location coordinates and velocity vectors. The two encoded features are then merged and passed to the decoder which generates future paths of pedestrians. Our extensive experimental results demonstrate competitive prediction performance from our method compared to state-of-art methods.

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Authors and Affiliations

  1. The University of Western Australia, Perth, Australia

    Hao Xue, Du Q. Huynh & Mark Reynolds

Authors
  1. Hao Xue
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  2. Du Q. Huynh
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  3. Mark Reynolds
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Corresponding author

Correspondence to Hao Xue .

Editor information

Editors and Affiliations

  1. Department of Computing, Macquarie University, Sydney, NSW, Australia

    Abhaya C. Nayak

  2. RIKEN Center for Integrative Medical Sciences, Yokohama, Japan

    Alok Sharma

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Xue, H., Huynh, D.Q., Reynolds, M. (2019). Pedestrian Trajectory Prediction Using a Social Pyramid. In: Nayak, A., Sharma, A. (eds) PRICAI 2019: Trends in Artificial Intelligence. PRICAI 2019. Lecture Notes in Computer Science(), vol 11671. Springer, Cham. https://doi.org/10.1007/978-3-030-29911-8_34

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  • DOI: https://doi.org/10.1007/978-3-030-29911-8_34

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-29910-1

  • Online ISBN: 978-3-030-29911-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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