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.
Abstract (translated)
寻找在多样场景中准确识别主题的稳健Person识别（Re-ID）系统仍然是一项艰巨的挑战，尤其是在监视和安全性应用中。本研究介绍了一种通过将不确定度特征融合（UFFM）与智能距离聚合（WDA）相结合来显著增强Person Re-Identification（Re-ID）的新方法。在基准数据集- Market-1501、DukeMTMC-ReID和MSMT17上进行了测试，我们的方法在排名1准确性和平均精度（mAP）方面取得了显著改进。具体来说，UFFM利用多个图像的特征合成能力克服了在不同视角下主题外观变异性所施加的局限性。WDA通过智能聚合相似度度量进一步优化了过程，从而增强了系统在识别主题间微小但关键差异的能力。实证结果证实了我们的方法优越于现有方法，在所有评估数据集上都实现了新的性能基准。代码可在Github上获取。
URL
https://arxiv.org/abs/2405.01101
