Abstract
Pedestrian trajectory prediction in crowd scenes is very useful in many applications such as video surveillance, self-driving cars, and robotic systems; however, it remains a challenging task because of the complex interactions and uncertainties of crowd motions. In this paper, a novel trajectory prediction method called the Attention-based Interaction-aware Spatio-temporal Graph Neural Network (AST-GNN) is proposed. AST-GNN uses an Attention mechanism to capture the complex interactions among multiple pedestrians. The attention mechanism allows for a dynamic and adaptive summary of the interactions of the nearby pedestrians. When the attention matrix is obtained, it is formulated into a propagation matrix for graph neural networks. Finally, a Time-extrapolator Convolutional Neural Network (TXP-CNN) is used in the temporal dimension of the aggregated features to predict the future trajectories of the pedestrians. Experimental results on benchmark pedestrian datasets (ETH and UCY) reveal the competitive performances of AST-GNN in terms of both the final displace error (FDE) and average displacement error (ADE) as compared with state-of-the-art trajectory prediction methods.
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Acknowledgements
This work is supported partly by the National Natural Science Foundation (NSFC) of China (grants 61973301, 61972020, 61633009, 51579053, 61772373 and U1613213), partly by the National Key R&D Program of China (grants 2016YFC0300801 and 2017YFB1300202), partly by the Field Fund of the 13th Five-Year Plan for Equipment Pre-research Fund (No. 61403120301), partly by Beijing Science and Technology Plan Project, partly by the Key Basic Research Project of Shanghai Science and Technology Innovation Plan (No. 15JC1403300), partly by Beijing Science and Technology Project. (No. Z181100008918018), partly by Beijing Nova Program (No. Z201100006820046), and partly by Meituan Open R&D Fund.
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Zhou, H., Ren, D., Xia, H., Fan, M., Yang, X., Huang, H. (2020). An Attention-Based Interaction-Aware Spatio-Temporal Graph Neural Network for Trajectory Prediction. In: Yang, H., Pasupa, K., Leung, A.CS., Kwok, J.T., Chan, J.H., King, I. (eds) Neural Information Processing. ICONIP 2020. Communications in Computer and Information Science, vol 1333. Springer, Cham. https://doi.org/10.1007/978-3-030-63823-8_5
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