Enhancing person re-identification via Uncertainty Feature Fusion Method and Auto-weighted Measure Combination
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866916718419378176 |
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| author | Che, Quang-Huy Nguyen, Le-Chuong Luu, Duc-Tuan Nguyen, Vinh-Tiep |
| author_facet | Che, Quang-Huy Nguyen, Le-Chuong Luu, Duc-Tuan Nguyen, Vinh-Tiep |
| contents | Person re-identification (Re-ID) is a challenging task that involves identifying the same person across different camera views in surveillance systems. Current methods usually rely on features from single-camera views, which can be limiting when dealing with multiple cameras and challenges such as changing viewpoints and occlusions. In this paper, a new approach is introduced that enhances the capability of ReID models through the Uncertain Feature Fusion Method (UFFM) and Auto-weighted Measure Combination (AMC). UFFM generates multi-view features using features extracted independently from multiple images to mitigate view bias. However, relying only on similarity based on multi-view features is limited because these features ignore the details represented in single-view features. Therefore, we propose the AMC method to generate a more robust similarity measure by combining various measures. Our method significantly improves Rank@1 accuracy and Mean Average Precision (mAP) when evaluated on person re-identification datasets. Combined with the BoT Baseline on challenging datasets, we achieve impressive results, with a 7.9% improvement in Rank@1 and a 12.1% improvement in mAP on the MSMT17 dataset. On the Occluded-DukeMTMC dataset, our method increases Rank@1 by 22.0% and mAP by 18.4%. Code is available: https://github.com/chequanghuy/Enhancing-Person-Re-Identification-via-UFFM-and-AMC |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_01101 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Enhancing person re-identification via Uncertainty Feature Fusion Method and Auto-weighted Measure Combination Che, Quang-Huy Nguyen, Le-Chuong Luu, Duc-Tuan Nguyen, Vinh-Tiep Computer Vision and Pattern Recognition Person re-identification (Re-ID) is a challenging task that involves identifying the same person across different camera views in surveillance systems. Current methods usually rely on features from single-camera views, which can be limiting when dealing with multiple cameras and challenges such as changing viewpoints and occlusions. In this paper, a new approach is introduced that enhances the capability of ReID models through the Uncertain Feature Fusion Method (UFFM) and Auto-weighted Measure Combination (AMC). UFFM generates multi-view features using features extracted independently from multiple images to mitigate view bias. However, relying only on similarity based on multi-view features is limited because these features ignore the details represented in single-view features. Therefore, we propose the AMC method to generate a more robust similarity measure by combining various measures. Our method significantly improves Rank@1 accuracy and Mean Average Precision (mAP) when evaluated on person re-identification datasets. Combined with the BoT Baseline on challenging datasets, we achieve impressive results, with a 7.9% improvement in Rank@1 and a 12.1% improvement in mAP on the MSMT17 dataset. On the Occluded-DukeMTMC dataset, our method increases Rank@1 by 22.0% and mAP by 18.4%. Code is available: https://github.com/chequanghuy/Enhancing-Person-Re-Identification-via-UFFM-and-AMC |
| title | Enhancing person re-identification via Uncertainty Feature Fusion Method and Auto-weighted Measure Combination |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2405.01101 |