SegDQ: Segmentation assisted multi-object tracking with dynamic query-based transformers | |
Liu, Yating1,2![]() ![]() ![]() ![]() ![]() | |
Source Publication | NEUROCOMPUTING
![]() |
ISSN | 0925-2312 |
2022-04-07 | |
Volume | 481Pages:91-101 |
Abstract | Multi-Object Tracking (MOT) has been one of the most important topics in computer vision. The tradi-tional tracking-by-detection framework of MOT is severely suffered from the poor detection results. In this paper, based on Transformer, we introduce the tracking-by-query MOT framework, and propose to apply semantic segmentation as an auxiliary task to optimize the training of MOT trackers, which addresses more on extracted foreground features. In addition, a feature-dependent dynamic object query (DOQ), instead of a fixed-learned object query (LOQ), is put forward to retrieve the new detections, improving the flexibility and constringency of the framework. We tested our SegDQ method on various scenarios including MOTChallenge 15, 16 and 17 datasets. The experimental results show that it obvi-ously improves the MOTA and IDF1 indexes of tracking results. (c) 2022 Published by Elsevier B.V. |
Keyword | Multi-object tracking Transformer Semantic task Dynamic query |
DOI | 10.1016/j.neucom.2022.01.073 |
WOS Keyword | ONLINE TRACKING |
Indexed By | SCI |
Language | 英语 |
Funding Project | National Natural Science Foundation of China[62173329] ; Intel Collaborative Research Institute for Intelligent and Automated Connected Vehicles (ICRI-IACV) |
Funding Organization | National Natural Science Foundation of China ; Intel Collaborative Research Institute for Intelligent and Automated Connected Vehicles (ICRI-IACV) |
WOS Research Area | Computer Science |
WOS Subject | Computer Science, Artificial Intelligence |
WOS ID | WOS:000761785300009 |
Publisher | ELSEVIER |
Citation statistics | |
Document Type | 期刊论文 |
Identifier | http://ir.ia.ac.cn/handle/173211/48036 |
Collection | 复杂系统管理与控制国家重点实验室_平行智能技术与系统团队 |
Corresponding Author | Wang, Fei-Yue |
Affiliation | 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.Univ Sci & Technol China, Dept Automat, Hefei 230027, Peoples R China 4.Qingdao Acad Intelligent Ind, Qingdao 266000, Peoples R China |
First Author Affilication | Institute of Automation, Chinese Academy of Sciences |
Corresponding Author Affilication | Institute of Automation, Chinese Academy of Sciences |
Recommended Citation GB/T 7714 | Liu, Yating,Bai, Tianxiang,Tian, Yonglin,et al. SegDQ: Segmentation assisted multi-object tracking with dynamic query-based transformers[J]. NEUROCOMPUTING,2022,481:91-101. |
APA | Liu, Yating.,Bai, Tianxiang.,Tian, Yonglin.,Wang, Yutong.,Wang, Jiangong.,...&Wang, Fei-Yue.(2022).SegDQ: Segmentation assisted multi-object tracking with dynamic query-based transformers.NEUROCOMPUTING,481,91-101. |
MLA | Liu, Yating,et al."SegDQ: Segmentation assisted multi-object tracking with dynamic query-based transformers".NEUROCOMPUTING 481(2022):91-101. |
Files in This Item: | Download All | |||||
File Name/Size | DocType | Version | Access | License | ||
1-s2.0-S092523122200(2635KB) | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | View Download |
Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.
Edit Comment