MDST-DGCN: A Multilevel Dynamic Spatiotemporal Directed Graph Convolutional Network for Pedestrian Trajectory Prediction

doi:10.1155/2022/4192367

	MDST-DGCN: A Multilevel Dynamic Spatiotemporal Directed Graph Convolutional Network for Pedestrian Trajectory Prediction
	Liu, Shaohua 1; Liu, Haibo 1; Wang, Yisu 1; Sun, Jingkai 1; Mao, Tianlu 2
	2022-04-12
发表期刊	COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE
ISSN	1687-5265
卷号	2022 页码:10
摘要	Pedestrian trajectory prediction is an essential but challenging task. Social interactions between pedestrians have an immense impact on trajectories. A better way to model social interactions generally achieves a more accurate trajectory prediction. To comprehensively model the interactions between pedestrians, we propose a multilevel dynamic spatiotemporal digraph convolutional network (MDST-DGCN). It consists of three parts: a motion encoder to capture the pedestrians' specific motion features, a multilevel dynamic spatiotemporal directed graph encoder (MDST-DGEN) to capture the social interaction features of multiple levels and adaptively fuse them, and a motion decoder to produce the future trajectories. Experimental results on public datasets demonstrate that our model achieves state-of-the-art results in both long-term and short-term predictions for both high-density and low-density crowds.
DOI	10.1155/2022/4192367
收录类别	SCI
语种	英语
资助项目	Major Program of the National Natural Science Foundation of China[91938301] ; National Natural Science Foundation of China[62002345]
WOS研究方向	Mathematical & Computational Biology ; Neurosciences & Neurology
WOS类目	Mathematical & Computational Biology ; Neurosciences
WOS记录号	WOS:000791773700005
出版者	HINDAWI LTD
引用统计	被引频次：2[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/19539
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Mao, Tianlu
作者单位	1.Beijing Univ Posts & Telecommun, Sch Elect Engn, Beijing 100876, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, Beijing Key Lab Mobile Comp & Pervas Device, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Liu, Shaohua,Liu, Haibo,Wang, Yisu,et al. MDST-DGCN: A Multilevel Dynamic Spatiotemporal Directed Graph Convolutional Network for Pedestrian Trajectory Prediction[J]. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE,2022,2022:10.
APA	Liu, Shaohua,Liu, Haibo,Wang, Yisu,Sun, Jingkai,&Mao, Tianlu.(2022).MDST-DGCN: A Multilevel Dynamic Spatiotemporal Directed Graph Convolutional Network for Pedestrian Trajectory Prediction.COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE,2022,10.
MLA	Liu, Shaohua,et al."MDST-DGCN: A Multilevel Dynamic Spatiotemporal Directed Graph Convolutional Network for Pedestrian Trajectory Prediction".COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022(2022):10.