期刊
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
卷 23, 期 12, 页码 24578-24587出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TITS.2022.3190959
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
资金
- National Science Foundation of China [NSFC 61906194]
- National Key Research and Development Program of China [2021YFF0602101]
This paper proposes a novel Salience-Navigated Vehicle Re-identification Network (SVRN) that explores diverse salient features at multi-scales. It tackles the limitation of traditional methods by mining sufficient salient features. Extensive experiments demonstrate its effectiveness and show superior results compared to previous state-of-the-art methods.
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).
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