Enhancing Person Re-Identification via Uncertainty Feature Fusion and Auto-weighted Measure Combination

• URL: http://arxiv.org/abs/2405.01101v4
• Date: Fri, 06 Dec 2024 19:42:48 GMT
• Title: Enhancing Person Re-Identification via Uncertainty Feature Fusion and Auto-weighted Measure Combination
• Authors: Quang-Huy Che, Le-Chuong Nguyen, Duc-Tuan Luu, Vinh-Tiep Nguyen,
• Abstract summary: Person re-identification (Re-ID) is a challenging task that involves identifying the same person across different camera views in surveillance systems. 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) Our method significantly improves Rank@1 accuracy and Mean Average Precision (mAP) when evaluated on person re-identification datasets.
• Score: 1.183049138259841
• License:
• Abstract: 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: \url{https://github.com/chequanghuy/Enhancing-Person-Re-Identification-via-UFFM-and-AMC}

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