Enhancing Person Re-Identification via Uncertainty Feature Fusion and Wise Distance Aggregation

• URL: http://arxiv.org/abs/2405.01101v1
• Date: Thu, 2 May 2024 09:09:48 GMT
• Title: Enhancing Person Re-Identification via Uncertainty Feature Fusion and Wise Distance Aggregation
• Authors: Quang-Huy Che, Le-Chuong Nguyen, Vinh-Tiep Nguyen,
• Abstract summary: This study presents a novel methodology that significantly enhances Person Re-Identification (Re-ID) Tested on benchmark datasets - Market-1501, DukeMTMC-ReID, and MSMT17 - our approach demonstrates substantial improvements in Rank-1 accuracy and mean Average Precision (mAP) UFFM capitalizes on the power of feature synthesis from multiple images to overcome the limitations imposed by the variability of subject appearances across different views. WDA further refines the process by intelligently aggregating similarity metrics, thereby enhancing the system's ability to discern subtle but critical differences between subjects.
• Score: 1.2923961938782627
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: The quest for robust Person re-identification (Re-ID) systems capable of accurately identifying subjects across diverse scenarios remains a formidable challenge in surveillance and security applications. This study presents a novel methodology that significantly enhances Person Re-Identification (Re-ID) by integrating Uncertainty Feature Fusion (UFFM) with Wise Distance Aggregation (WDA). Tested on benchmark datasets - Market-1501, DukeMTMC-ReID, and MSMT17 - our approach demonstrates substantial improvements in Rank-1 accuracy and mean Average Precision (mAP). Specifically, UFFM capitalizes on the power of feature synthesis from multiple images to overcome the limitations imposed by the variability of subject appearances across different views. WDA further refines the process by intelligently aggregating similarity metrics, thereby enhancing the system's ability to discern subtle but critical differences between subjects. The empirical results affirm the superiority of our method over existing approaches, achieving new performance benchmarks across all evaluated datasets. Code is available on Github.

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