Person Re-identification (ReID) is the process of matching target individuals to their images within different images or videos captured from a variety of angles or cameras. This is a critical task for surveillance applications, in particular, these applications that operate in large environments such as malls and airports. Recent studies use data-driven approaches to tackle this problem. This work continues on this path by presenting a modification of a previously defined loss, the centroid triplet loss ( CTL). The proposed loss, modified centroid triplet loss (MCTL), emphasizes more on the interclass distance. It is divided into two parts, one penalizes for interclass distance and second penalizes for intraclass distance. Mean Average Precision (mAP) was adopted to validate our approach, two datasets are also used for validation; Market-1501 and DukeMTMC. The results were calculated for first rank of identification and mAP. For dataset Market-1501 dataset, the results were \(98.4\%\) rank1, \(98.63\%\) mAP, and \(96.8\%\) rank1, \(97.3\%\) mAP on DukeMTMC dataset, the results outweighed those of existing studies in the domain.

行人再识别 (ReID) 是将目标个体与其在从各种角度或摄像机捕获的不同图像或视频中的图像进行匹配的过程。这是监控应用程序的一项关键任务，尤其是在大型环境（例如商场和机场）中运行的这些应用程序。最近的研究使用数据驱动的方法来解决这个问题。这项工作通过对先前定义的损失进行修改，即质心三重态损失 (CTL)，继续沿着这条道路前进。提议的损失，改进的质心三元组损失（MCTL），更强调类间距离。它分为两部分，一是对类间距离进行惩罚，二是对类内距离进行惩罚。采用平均精度（mAP）来验证我们的方法，两个数据集也用于验证；Market-1501 和 DukeMTMC。计算一级鉴定和mAP的结果。对于数据集 Market-1501 数据集，结果是\(98.4\%\) rank1, \(98.63\%\) mAP 和\(96.8\%\) rank1, \(97.3\%\) mAP 在 DukeMTMC 数据集上，结果超过了该领域现有研究的结果.