| Navigating Diverse Salient Features for Vehicle Re-Identification | |
Qian, Wen1,2; He, Zhiqun3; Chen, Chen1,2 ; Peng, Silong1,2
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| Source Publication | IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
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| ISSN | 1524-9050 |
| 2022-07-22 | |
| Pages | 10 |
| Corresponding Author | Chen, Chen(chen.chen@ia.ac.cn) |
| Abstract | Mining sufficient discriminative information is vital for effective feature representation in vehicle re-identification. Traditional methods mainly focus on the most salient features and neglect whether the explored information is sufficient. This paper tackles the above limitation by proposing a novel Salience-Navigated Vehicle Re-identification Network (SVRN) which explores diverse salient features at multi-scales. For mining sufficient salient features, we design SVRN from two aspects: 1) network architecture: we propose a novel salience-navigated vehicle re-identification network, which mines diverse features under a cascaded suppress-and-explore mode. 2) feature space: cross-space constraint enables the diversity from feature space, which restrains the cross-space features by vehicle and image identifications (IDs). Extensive experiments demonstrate our method's effectiveness, and the overall results surpass all previous state-of-the-arts in three widely-used Vehicle ReID benchmarks (VeRi-776, VehicleID, and VERI-WILD), i.e., we achieve an 84.5% mAP on VeRi-776 benchmark that outperforms the second-best method by a large margin (3.5% mAP). |
| Keyword | Navigation Task analysis Image color analysis Boosting Feature extraction Benchmark testing Space vehicles Vehicle re-identification suppress-and-explore mode grid-based salient navigation cross-space constraints |
| DOI | 10.1109/TITS.2022.3190959 |
| Indexed By | SCI |
| Language | 英语 |
| Funding Project | National Science Foundation of China[NSFC 61906194] ; National Key Research and Development Program of China[2021YFF0602101] |
| Funding Organization | National Science Foundation of China ; National Key Research and Development Program of China |
| WOS Research Area | Engineering ; Transportation |
| WOS Subject | Engineering, Civil ; Engineering, Electrical & Electronic ; Transportation Science & Technology |
| WOS ID | WOS:000833053100001 |
| Publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.ia.ac.cn/handle/173211/49771 |
| Collection | 智能制造技术与系统研究中心_多维数据分析 |
| Corresponding Author | Chen, Chen |
| Affiliation | 1.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Huairou 101408, Peoples R China 3.Sensetime, Shenzhen 518000, 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 | Qian, Wen,He, Zhiqun,Chen, Chen,et al. Navigating Diverse Salient Features for Vehicle Re-Identification[J]. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS,2022:10. |
| APA | Qian, Wen,He, Zhiqun,Chen, Chen,&Peng, Silong.(2022).Navigating Diverse Salient Features for Vehicle Re-Identification.IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS,10. |
| MLA | Qian, Wen,et al."Navigating Diverse Salient Features for Vehicle Re-Identification".IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2022):10. |
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