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Volume 7 Issue 4
Jun.  2020

IEEE/CAA Journal of Automatica Sinica

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Xiaodong Zhao, Yaran Chen, Jin Guo and Dongbin Zhao, "A Spatial-Temporal Attention Model for Human Trajectory Prediction," IEEE/CAA J. Autom. Sinica, vol. 7, no. 4, pp. 965-974, July 2020. doi: 10.1109/JAS.2020.1003228
Citation: Xiaodong Zhao, Yaran Chen, Jin Guo and Dongbin Zhao, "A Spatial-Temporal Attention Model for Human Trajectory Prediction," IEEE/CAA J. Autom. Sinica, vol. 7, no. 4, pp. 965-974, July 2020. doi: 10.1109/JAS.2020.1003228

A Spatial-Temporal Attention Model for Human Trajectory Prediction

doi: 10.1109/JAS.2020.1003228
Funds:  This work was supported by the National Key Research and Development Program of China (2018AAA0101005, 2018AAA0102404), the Program of the Huawei Technologies Co. Ltd. (FA2018111061SOW12), the National Natural Science Foundation of China (61773054), and the Youth Research Fund of the State Key Laboratory of Complex Systems Management and Control (20190213)
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  • Human trajectory prediction is essential and promising in many related applications. This is challenging due to the uncertainty of human behaviors, which can be influenced not only by himself, but also by the surrounding environment. Recent works based on long-short term memory (LSTM) models have brought tremendous improvements on the task of trajectory prediction. However, most of them focus on the spatial influence of humans but ignore the temporal influence. In this paper, we propose a novel spatial-temporal attention (ST-Attention) model, which studies spatial and temporal affinities jointly. Specifically, we introduce an attention mechanism to extract temporal affinity, learning the importance for historical trajectory information at different time instants. To explore spatial affinity, a deep neural network is employed to measure different importance of the neighbors. Experimental results show that our method achieves competitive performance compared with state-of-the-art methods on publicly available datasets.

     

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    Highlights

    • Study the trajectory prediction jointly with temporal and spatial affinities.
    • A LSTM model that uses attention mechanism to improve the accuracy of trajectory prediction .
    • An experimental error analysis using data based on both world plane and image plane.

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