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Discriminative-region attention and orthogonal-view generation model for vehicle re-identification

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Abstract

Vehicle re-identification (Re-ID) is urgently demanded to alleviate the pressure caused by the increasingly onerous task of urban traffic management. Multiple challenges hamper the applications of vision-based vehicle Re-ID methods: (1) The appearances of different vehicles of the same brand/model are often similar; However, (2) the appearances of the same vehicle differ significantly from different viewpoints. Previous methods mainly use manually annotated multi-attribute datasets to assist the network in getting detailed cues and in inferencing multi-view to improve the vehicle Re-ID performance. However, finely labeled vehicle datasets are usually unattainable in real application scenarios. Hence, we propose a Discriminative-Region Attention and Orthogonal-View Generation (DRA-OVG) model, which only requires identity (ID) labels to conquer the multiple challenges of vehicle Re-ID. The proposed DRA model can automatically extract the discriminative region features, which can distinguish similar vehicles. And the OVG model can generate multi-view features based on the input view features to reduce the impact of viewpoint mismatches. Finally, the distance between vehicle appearances is presented by the discriminative region features and multi-view features together. Therefore, the significance of pairwise distance measure between vehicles is enhanced in a complete feature space. Extensive experiments substantiate the effectiveness of each proposed ingredient, and experimental results indicate that our approach achieves remarkable improvements over the state-of-the-art vehicle Re-ID methods on VehicleID and VeRi-776 datasets.

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Funding

This work is supported by the National Nature Science Foundation of China (grant No.61871106 and No.61370152), Key R & D projects of Liaoning Province, China (grant No. 2020JH2/10100029), and the Open Project Program Foundation of the Key Laboratory of Opto-Electronics Information Processing, Chinese Academy of Sciences (OEIP-O-202002).

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Authors and Affiliations

  1. College of Information Science and Engineering, Northeastern University, Nanhu, Shenyang, 110819, Liaoning, China

    Huadong Li, Yuefeng Wang, Ying Wei & Lin Wang

  2. Peng Cheng Laboratory, Xingke 1st, Shenzhen, 518000, Guangdong, China

    Huadong Li & Ge Li

  3. Information Technology R&D Innovation Center of Peking University, Peking University, Gaobu, Shaoxing, 312000, Zhejiang, China

    Yuefeng Wang & Ying Wei

  4. School of Electronics and Computer Engineering, Peking University Shenzhen Graduate School, Lishui, Shenzhen, 518055, Guangdong, China

    Ge Li

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  1. Huadong Li
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  4. Lin Wang
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Correspondence to Ying Wei.

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Li, H., Wang, Y., Wei, Y. et al. Discriminative-region attention and orthogonal-view generation model for vehicle re-identification. Appl Intell 53, 186–203 (2023). https://doi.org/10.1007/s10489-022-03420-8

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