research-article

Prediction of Pedestrian Trajectory in a Crowded Environment Using RNN Encoder-Decoder

Authors:

Xiong Xincheng,

Eam Khwang Teoh,

Wei-Yun YauAuthors Info & Claims

ICRAI '19: Proceedings of the 5th International Conference on Robotics and Artificial Intelligence

Pages 64 - 69

https://doi.org/10.1145/3373724.3373729

Published: 03 February 2020 Publication History

Abstract

Deep learning is the current state-of-the-art technique for most machine learning and data analytics tasks. It has been applied to all aspects of human life. Traditional methods are not competitive for solving pedestrian trajectory prediction problems due to the diversity of its environment and the uncertainty of the original trajectory. Due to the great success of the RNN (Recurrent Neural Networks) architecture in sequence prediction, it has become the first choice to us for solving pedestrian trajectory prediction problems in a crowded environment. In this work, in order to solve the problem that RNN architecture does not have great accuracy when the input is long sequence, we use LSTM (Long Short Term Memory) which produces higher accuracy when the input is long sequence. In the process of building our LSTM based prediction model, we find the best loss function through experiments and data analysis. Then we try various model hyperparameters combinations and find best hyperparameter values and parameter ranges that would make the prediction results more accurate. At the same time, we creatively use velocity values of pedestrian trajectory rather than coordinate values as input of the model. Experiments on several datasets shows that the proposed approach achieves high accuracy when it predicts pedestrian trajectory in a crowded environment.

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Digital Library

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Alahi, A., Goel, K., Ramanathan, V., Robicquet, A., Fei-Fei, L. and Savarese, S. 2016. Social lstm: Human trajectory prediction in crowded spaces. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 961--971.

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Cited By

Liu LXi ZZhu KWang RHossain E(2022)Mobile Charging Station Placements in Internet of Electric Vehicles: A Federated Learning ApproachIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2022.320559623:12(24561-24577)Online publication date: Dec-2022
https://doi.org/10.1109/TITS.2022.3205596

Index Terms

Prediction of Pedestrian Trajectory in a Crowded Environment Using RNN Encoder-Decoder
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Tracking

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ICRAI '19: Proceedings of the 5th International Conference on Robotics and Artificial Intelligence

November 2019

108 pages

ISBN:9781450372350

DOI:10.1145/3373724

Copyright © 2019 ACM.

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Published: 03 February 2020

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ICRAI '19

ICRAI '19: 2019 5th International Conference on Robotics and Artificial Intelligence

November 22 - 24, 2019

Singapore, Singapore

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Cited By

Liu LXi ZZhu KWang RHossain E(2022)Mobile Charging Station Placements in Internet of Electric Vehicles: A Federated Learning ApproachIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2022.320559623:12(24561-24577)Online publication date: Dec-2022
https://doi.org/10.1109/TITS.2022.3205596

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