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A Spatial-Temporal Attention Model forHuman Trajectory Prediction
Xiaodong Zhao; Yaran Chen; Jin Guo; Dongbin Zhao
发表期刊IEEE/CAA Journal of Automatica Sinica
ISSN2329-9266
2020
卷号7期号:4页码:965-974
摘要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.
关键词Attention mechanism long-short term memory (LSTM) spatial-temporal model trajectory prediction
DOI10.1109/JAS.2020.1003228
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被引频次:44[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/43005
专题学术期刊_IEEE/CAA Journal of Automatica Sinica
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Xiaodong Zhao,Yaran Chen,Jin Guo,et al. A Spatial-Temporal Attention Model forHuman Trajectory Prediction[J]. IEEE/CAA Journal of Automatica Sinica,2020,7(4):965-974.
APA Xiaodong Zhao,Yaran Chen,Jin Guo,&Dongbin Zhao.(2020).A Spatial-Temporal Attention Model forHuman Trajectory Prediction.IEEE/CAA Journal of Automatica Sinica,7(4),965-974.
MLA Xiaodong Zhao,et al."A Spatial-Temporal Attention Model forHuman Trajectory Prediction".IEEE/CAA Journal of Automatica Sinica 7.4(2020):965-974.
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