利用全局与局部关联特征的行人重识别方法

Person re-identification method based on global and local relation features

针对因行人图像背景差异大、人体外观相似导致的行人再识别准确率低的问题,提出了一种利用特征融合与多尺度信息的行人重识别方法。首先,通过ResNet50_IBN提取人体图像全局特征图。其次,设计分支结构,第1分支利用空间变换网络对全局特征图进行自适应的空间特征对齐,水平切分全局特征图得到局部特征,采用全局特征与每个局部特征分别融合的方式来挖掘特征之间的关联关系。第2分支增加了4种不同尺度的卷积层提取全局图像的多尺度特征。最后,在推理阶段将第1分支和第2分支的特征进行通道维度的串联,作为行人的对比特征。通过在Market-1501、DukeMTMC数据集上的实验表明,所提方法与AlignedReID和EA-Net等特征对齐和局部特征提取方法相比具备更强的性能,在Market-1501上,mAP和Rank-1分别达到了86.77%和94.83%。

A person re-identification method based on feature fusion and multi-scale information was proposed to solve the problem of low accuracy of person re-identification due to the large difference of human image background and similar global appearance of human body. Firstly, the global feature map of human body image is extracted by ResNet50. Secondly, the branch structure is designed. In the first branch, the spatial transformation network is used to align the global feature images adaptively, and the local feature images are obtained by horizontal segmentation of the global feature images. The correlation between the global feature and each local feature is mined by fusing the global feature and each local feature separately. The second branch adds four convolution layers of different scales to extract multi-scale features from global images. Finally, in the reasoning stage, the features of the first branch and the second branch are connected in series as the comparative features of person. Experiments on the Market-1501 and DukeMTMC datasets show that the proposed method has better performance than the AlignedReID and EA-NET feature alignment and local feature extraction methods. In the Market-1501 dataset, mAP and Rank-1 reach 86.77% and 94.83%, respectively.