A spatial-temporal attention model for human trajectory prediction

doi:10.1109/JAS.2020.1003228

CORC > 自动化研究所 > 中国科学院自动化研究所 > 复杂系统管理与控制国家重点实验室 > 深度强化学习

	A spatial-temporal attention model for human trajectory prediction
	Zhao, Xiaodong 2,3; Chen, Yaran 3; Guo, Jin 1,2; Zhao, Dongbin 3
刊名	IEEE-CAA JOURNAL OF AUTOMATICA SINICA
	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
DOI	10.1109/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
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000545416200005
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
内容类型	期刊论文
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.Minist Educ, Key Lab Knowledge Automat Ind Proc, Beijing 100083, Peoples R China 2.Univ Sci & Technol Beijing, Sch Automat & Elect Engn, Beijing 100083, Peoples R China 3.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, 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.