Person re-identification aims to match images of the same person across
disjoint camera views, which is a challenging problem in video surveillance.
The major challenge of this task lies in how to preserve the similarity of the
same person against large variations caused by complex backgrounds, mutual
occlusions and different illuminations, while discriminating the different
individuals. In this paper, we present a novel deep ranking model with feature
learning and fusion by learning a large adaptive margin between the intra-class
distance and inter-class distance to solve the person re-identification
problem. Specifically, we organize the training images into a batch of pairwise
samples. Treating these pairwise samples as inputs, we build a novel part-based
deep convolutional neural network (CNN) to learn the layered feature
representations by preserving a large adaptive margin. As a result, the final
learned model can effectively find out the matched target to the anchor image
among a number of candidates in the gallery image set by learning
discriminative and stable feature representations. Overcoming the weaknesses of
conventional fixed-margin loss functions, our adaptive margin loss function is
more appropriate for the dynamic feature space. On four benchmark datasets,
PRID2011, Market1501, CUHK01 and 3DPeS, we extensively conduct comparative
evaluations to demonstrate the advantages of the proposed method over the
state-of-the-art approaches in person re-identification.

本篇论文提出了一种基于深度排名模型、部分卷积神经网络以及自适应距离边缘损失函数相结合的方法，可以在多个数据集上有效地解决视频监控中的人员重识别问题，并且相较于现有的人员重识别方法具有更好的性能表现。