融合RGB与灰度图像特征的行人再识别方法

Pedestrian Re-Identification Method Combining RGB and Grayscale Image Features

针对行人再识别过程中相同身份行人图像颜色不一致,以及不同身份行人图像颜色相近问题,提出一种基于双分支残差网络的行人再识别方法。将RGB图像和灰度图像分别输入预训练的ResNet-50网络,获得RGB图像特征和灰度图像特征并对其进行融合,利用统一水平划分策略学习融合特征,同时将RGB特征、灰度特征和融合特征的拼接结果作为最终特征表示。在Market1501、DukeMTMC-ReID和CUHK03数据集上的实验结果表明,与PCB、Mancs等行人再识别方法相比,该方法的平均精度均值和首位命中率更高,且对于图像颜色变化具有更强的鲁棒性。

To address the problems in pedestrain Re-Identification(ReID),including color inconsistency of pedestrain images with the same identity and color similarity between pedestrain images with different identities,this paper proposes a pedestrain ReID method based on double branch residual network.The RGB images and grayscale images are input into the pre-trained ResNet-50 separately to obtain RGB and grayscale features that are subsequently fused.Then,the fusion features are learned by using a unified horizontal partition strategy.Finally,the RGB,grayscale and fusion features are concatenated to act as the final feature representation.Experimental results on Market1501,DukeMTMCReID and CUHK03 datasets show that the proposed method has mean Average Precision(mAP)and Rank-1 accuracy than PCB,Mancs and other pedestrian ReID methods,and has stronger robustness to image color changes.