Navigating Diverse Salient Features for Vehicle Re-Identification
文献类型：期刊论文
| 作者 | Qian, Wen2,3 ; He, Zhiqun1; Chen, Chen2,3; Peng, Silong2,3
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| 刊名 | IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
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| 出版日期 | 2022-07-22 |
| 页码 | 10 |
| 关键词 | 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 |
| ISSN号 | 1524-9050 |
| DOI | 10.1109/TITS.2022.3190959 |
| 通讯作者 | Chen, Chen(chen.chen@ia.ac.cn) |
| 英文摘要 | 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). |
| 资助项目 | National Science Foundation of China[NSFC 61906194] ; National Key Research and Development Program of China[2021YFF0602101] |
| WOS研究方向 | Engineering ; Transportation |
| 语种 | 英语 |
| WOS记录号 | WOS:000833053100001 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | National Science Foundation of China ; National Key Research and Development Program of China |
| 源URL | [http://ir.ia.ac.cn/handle/173211/49770]  |
| 专题 | 自动化研究所_智能制造技术与系统研究中心_多维数据分析团队 |
| 通讯作者 | Chen, Chen |
| 作者单位 | 1.Sensetime, Shenzhen 518000, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Huairou 101408, Peoples R China 3.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China |
| 推荐引用方式 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. |
入库方式： OAI收割
来源：自动化研究所
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