التفاصيل البيبلوغرافية
| العنوان: |
Cross-Domain Person Re-Identification Based on Multi-Branch Pose-Guided Occlusion Generation |
| المؤلفون: |
Pengnan Liu, Yanchen Wang, Yunlong Li, Deqiang Cheng, Feixiang Xu |
| المصدر: |
Sensors, Vol 25, Iss 2, p 473 (2025) |
| بيانات النشر: |
MDPI AG, 2025. |
| سنة النشر: |
2025 |
| المجموعة: |
LCC:Chemical technology |
| مصطلحات موضوعية: |
cross-domain, person re-identification, pose-guided occlusion, multi-branch, Chemical technology, TP1-1185 |
| الوصف: |
Aiming at the problems caused by a lack of feature matching due to occlusion and fixed model parameters in cross-domain person re-identification, a method based on multi-branch pose-guided occlusion generation is proposed. This method can effectively improve the accuracy of person matching and enable identity matching even when pedestrian features are misaligned. Firstly, a novel pose-guided occlusion generation module is designed to enhance the model’s ability to extract discriminative features from non-occluded areas. Occlusion data are generated to simulate occluded person images. This improves the model’s learning ability and addresses the issue of misidentifying occlusion samples. Secondly, a multi-branch feature fusion structure is constructed. By fusing different feature information from the global and occlusion branches, the diversity of features is enriched. This enrichment improves the model’s generalization. Finally, a dynamic convolution kernel is constructed to calculate the similarity between images. This approach achieves effective point-to-point matching and resolves the problem of fixed model parameters. Experimental results indicate that, compared to current mainstream algorithms, this method shows significant advantages in the first hit rate (Rank-1), mean average precision (mAP), and generalization performance. In the MSMT17→DukeMTMC-reID dataset, after re-ranking (Rerank) and time-tift (Tlift) for the two indicators on Market1501, the mAP and Rank-1 reached 80.5%, 84.3%, 81.9%, and 93.1%. Additionally, the algorithm achieved 51.6% and 41.3% on DukeMTMC-reID→Occluded-Duke, demonstrating good recognition performance on the occlusion dataset. |
| نوع الوثيقة: |
article |
| وصف الملف: |
electronic resource |
| اللغة: |
English |
| تدمد: |
1424-8220 |
| Relation: |
https://www.mdpi.com/1424-8220/25/2/473; https://doaj.org/toc/1424-8220 |
| DOI: |
10.3390/s25020473 |
| URL الوصول: |
https://doaj.org/article/d45dc74aa55148a8a5e6fb02e677b8d6 |
| رقم الانضمام: |
edsdoj.45dc74aa55148a8a5e6fb02e677b8d6 |
| قاعدة البيانات: |
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