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A spatial-temporal attention model for human trajectory prediction

文献类型：期刊论文

作者Zhao, Xiaodong1,2; Chen, Yaran2; Guo, Jin1,3; Zhao, Dongbin2
刊名IEEE-CAA JOURNAL OF AUTOMATICA SINICA
出版日期2020-07-01
卷号7期号:4页码:965-974
关键词Attention mechanism long-short term memory (LSTM) spatial-temporal model trajectory prediction
ISSN号2329-9266
DOI10.1109/JAS.2020.1003228
通讯作者Chen, Yaran(chenyaran2013@ia.ac.cn) ; Guo, Jin(guojin@ustb.edu.cn)
英文摘要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.
资助项目National Key Research and Development Program of China[2018AAA0101005] ; National Key Research and Development Program of China[2018AAA0102404] ; Huawei Technologies Co. Ltd.[FA2018111061SOW12] ; National Natural Science Foundation of China[61773054] ; Youth Research Fund of the State Key Laboratory of Complex Systems Management and Control[20190213]
WOS研究方向Automation & Control Systems
语种英语
WOS记录号WOS:000545416200005
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
资助机构National Key Research and Development Program of China ; Huawei Technologies Co. Ltd. ; National Natural Science Foundation of China ; Youth Research Fund of the State Key Laboratory of Complex Systems Management and Control
源URL[http://ir.ia.ac.cn/handle/173211/40025]  
专题复杂系统管理与控制国家重点实验室_深度强化学习
通讯作者Chen, Yaran; Guo, Jin
作者单位1.Univ Sci & Technol Beijing, Sch Automat & Elect Engn, Beijing 100083, Peoples R China
2.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
3.Minist Educ, Key Lab Knowledge Automat Ind Proc, Beijing 100083, Peoples R China
推荐引用方式
GB/T 7714		 Zhao, Xiaodong,Chen, Yaran,Guo, Jin,et al. A spatial-temporal attention model for human trajectory prediction[J]. IEEE-CAA JOURNAL OF AUTOMATICA SINICA,2020,7(4):965-974.
APA Zhao, Xiaodong,Chen, Yaran,Guo, Jin,&Zhao, Dongbin.(2020).A spatial-temporal attention model for human trajectory prediction.IEEE-CAA JOURNAL OF AUTOMATICA SINICA,7(4),965-974.
MLA Zhao, Xiaodong,et al."A spatial-temporal attention model for human trajectory prediction".IEEE-CAA JOURNAL OF AUTOMATICA SINICA 7.4(2020):965-974.

入库方式： OAI收割

来源：自动化研究所

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