The Multi-Layer Constrained Loss for Cross-Modality Person Re-Identification
Zhanrui Sun, Yongxin Zhu, Shijin Song, Junjie Hou, Sen Du, Yuefeng Song · 2020
Person Re-identification (Re-ID) has great potential in video surveillance and security. At present, Re-ID in the visible light field has reached a relatively high precision, but the related research on infrared-visible cross-modality ReID problem for night monitoring still has plenty of room for improvement. Current methods mainly focus on extracting the features of thermal(infrared) and visible person images using two models, and then classifying and measuring learning through partial sharing weights and the same loss regression layer. We found that the main difference between infrared image and visible light lies in color and edge sharpening. In this paper, we proposed a simple and effective end-to-end crossmodality Re-ID model named cmPCB by using a Part-based Convolution-al Baseline (PCB) model for feature learning. We use Resnet network to extract network features, and we proposed a Multi-Layer Constrained (MLC) Loss, and extend the distance between near infrared and visible modes. Experimental results demonstrate that our model has superi-or performance compared to the state-of-the-arts, i.e. on SYSU- MM01 dataset(All-search multi-shot), Rank1 is improved from 43.86% to 65.83%, mAP is improved from 30.48% to 53.34%; on RegDB dataset, Rank1 is improved from 50.85% to 81.02%, mAP is improved from 48.67% to 78.73%.