| HUANG Chengti*(黄诚惕),ZHANG Xiaoxiang*,ZHAO Qianqian**,ZHU Jianqing*.[J].高技术通讯(英文),2025,31(1):32~40 |
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| Unsupervised vehicle re-identification via meta-type generalization |
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| DOI：10. 3772 / j. issn. 1006-6748. 2025. 01. 004 |
| 中文关键词: |
| 英文关键词: deep learning, unsupervised vehicle re-identification (Re-ID), meta-learning |
| 基金项目: |
| Author Name | Affiliation | | HUANG Chengti*(黄诚惕) | (*College of Engineering, Huaqiao University, Quanzhou 362021, P. R. China)
(**College of Information Science and Engineering, Huaqiao University, Xiamen 361021, P. R. China) | | ZHANG Xiaoxiang* | | | ZHAO Qianqian** | | | ZHU Jianqing* | |
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| 中文摘要: |
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| 英文摘要: |
| Unsupervised vehicle re-identification ( Re-ID) methods have garnered widespread attention due to their potential in real-world traffic monitoring. However, existing unsupervised domain adaptation techniques often rely on pseudo-labels generated from the source domain, which struggle to effectively address the diversity and dynamic nature of real-world scenarios. Given the limited variety of common vehicle types, enhancing the model’s generalization capability across these types is crucial. To this end, an innovative approach called meta-type generalization (MTG) is proposed. By dividing the training data into meta-train and meta-test sets based on vehicle type information, a novel gradient interaction computation strategy is designed to enhance the model’s ability to learn typeinvariant features. Integrated into the ResNet50 backbone, the MTG model achieves improvements of 4. 50% and 12. 04% on the Veri-776 and VRAI datasets, respectively, compared with traditional unsupervised algorithms, and surpasses current state-of-the-art methods. This achievement holds promise for application in intelligent traffic systems, enabling more efficient urban traffic solutions. |
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