Multi-object tracking with hard-soft attention network and group-based cost minimization

doi:10.1016/j.neucom.2021.02.084

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 > 先进控制与自动化团队

	Multi-object tracking with hard-soft attention network and group-based cost minimization
	Liu, Yating 2,3; Li, Xuesong 1; Bai, Tianxiang 2,3; Wang, Kunfeng 4; Wang, Fei-Yue 2
刊名	NEUROCOMPUTING
	2021-08-04
卷号	447 页码:80-91
关键词	Multi-object tracking Attention mechanism Unary and binary costs Appearance-motion affinity
ISSN号	0925-2312
DOI	10.1016/j.neucom.2021.02.084
通讯作者	Wang, Kunfeng(wangkf@mail.buct.edu.cn)
英文摘要	Multi-object tracking (MOT) has received constant attention from researchers with the development of deep learning and person re-identification (ReID). However, the occlusion caused tracking failure is still far from solved. In this paper, we propose a Hard-Soft Attention Network (HSAN) to improve the ReID performance and get robust appearance features of different targets. The pose information and attention mechanism are combined to distinguish between challenging targets. Besides, the unary and binary costs are constructed to ensure consistency and long-term tracking, which consider not only the appearance motion affinity of single tracks, but also the interactions between neighboring tracks. For that we cluster the tracks into different groups and choose reliable tracks as anchors to establish the two types of costs. Our HSAN appearance model is evaluated on the Market-1501, DUKE and CUHK03 ReID datasets and the MOT tracking method is conducted on MOTChallenge 15, 16 and 17. The experimental results demonstrate that our method can improve tracking accuracy and reduce fragments. (c) 2021 Elsevier B.V. All rights reserved.
资助项目	Intel Collaborative Research Institute for Intelligent and Automated Connected Vehicles (ICRIIACV) ; National Natural Science Foundation of China[62076020] ; National Natural Science Foundation of China[U1811463]
WOS关键词	PEOPLE
WOS研究方向	Computer Science
语种	英语
出版者	ELSEVIER
WOS记录号	WOS:000656962800007
资助机构	Intel Collaborative Research Institute for Intelligent and Automated Connected Vehicles (ICRIIACV) ; National Natural Science Foundation of China
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/45304]
专题	自动化研究所_复杂系统管理与控制国家重点实验室_先进控制与自动化团队
通讯作者	Wang, Kunfeng
作者单位	1.Key Lab Informat Syst Engn, Nanjing 210007, Peoples R China 2.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 3.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 4.Beijing Univ Chem Technol, Coll Informat Sci & Technol, Beijing 100029, Peoples R China
推荐引用方式 GB/T 7714	Liu, Yating,Li, Xuesong,Bai, Tianxiang,et al. Multi-object tracking with hard-soft attention network and group-based cost minimization[J]. NEUROCOMPUTING,2021,447:80-91.
APA	Liu, Yating,Li, Xuesong,Bai, Tianxiang,Wang, Kunfeng,&Wang, Fei-Yue.(2021).Multi-object tracking with hard-soft attention network and group-based cost minimization.NEUROCOMPUTING,447,80-91.
MLA	Liu, Yating,et al."Multi-object tracking with hard-soft attention network and group-based cost minimization".NEUROCOMPUTING 447(2021):80-91.