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Multi-object tracking with hard-soft attention network and group-based cost minimization | |
Liu, Yating1,2![]() ![]() ![]() ![]() ![]() | |
发表期刊 | NEUROCOMPUTING
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ISSN | 0925-2312 |
2021-08-04 | |
卷号 | 447页码:80-91 |
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. |
关键词 | Multi-object tracking Attention mechanism Unary and binary costs Appearance-motion affinity |
DOI | 10.1016/j.neucom.2021.02.084 |
关键词[WOS] | PEOPLE |
收录类别 | SCI |
语种 | 英语 |
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] |
项目资助者 | Intel Collaborative Research Institute for Intelligent and Automated Connected Vehicles (ICRIIACV) ; National Natural Science Foundation of China |
WOS研究方向 | Computer Science |
WOS类目 | Computer Science, Artificial Intelligence |
WOS记录号 | WOS:000656962800007 |
出版者 | ELSEVIER |
七大方向——子方向分类 | 目标检测、跟踪与识别 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/45305 |
专题 | 多模态人工智能系统全国重点实验室_平行智能技术与系统团队 |
通讯作者 | Wang, Kunfeng |
作者单位 | 1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 3.Key Lab Informat Syst Engn, Nanjing 210007, Peoples R China 4.Beijing Univ Chem Technol, Coll Informat Sci & Technol, Beijing 100029, Peoples R China |
第一作者单位 | 中国科学院自动化研究所 |
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. |
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