Modified centroid triplet loss for person re-identification

Abstract 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\% 98. 4 % rank1, 98. 63\% 98. 63 % mAP, and 96. 8\% 96. 8 % rank1, 97. 3\% 97. 3 % mAP on DukeMTMC dataset, the results outweighed those of existing studies in the domain.