基于深度卷积生成对抗网络和拓展近邻重排序的行人重识别

在刑事侦查、智能监控、图像检索等领域，行人重识别一直是研究的热点.由于现有的大部分方法依赖有标注数据集，因此标签的缺乏使得无监督的行人重识别技术变得更具挑战性.为了克服这一问题，提出了一个用于生成可靠伪标签的框架，这些生成标签可以为现有监督行人重识别模型提供监督信号.假设数据集内的大部分图片都满足同一个摄像头拍摄的图片差异主要在于前景（行人）、同一个行人被不同摄像头拍摄到的图片差异主要在于背景.为了消除图片背景带来的差异，首先把数据集中的图片依据摄像头编号分成若干个域，通过计算每个域内的图片间的欧式距离，建立图模型，执行最大团算法寻找最相似的若干个图片并认为它们属于同一个行人；紧接着计算不同摄像头域间的团的相似度，据此进行合并；最终给出全局伪标签.所提的框架无需人为标注数据，以一种无监督的方式运行，并在Market1501和DukeMTMC-ReID数据集上进行实验，实验发现所提方法比其他相关方法具有更高的精度，从而进一步证明了所提方法的有效性.

Person re-identification based on deep convolutional generative adversarial network and expanded neighbor reranking

In criminal investigation, intelligent monitoring, image retrieval and other fields, person re-identification has always been a hot and significant research topic. Since most of the existing methods rely on labeled datasets, the lack of labels makes unsupervised person re-identification technology more challenging. In order to overcome the problem of the lack of labels, a new framework to generate reliable pseudo labels is proposed as supervision information for existing supervised person re-identification models. Assuming that images taken by the same camera mainly vary in pedestrian’s physical appearances rather than backgrounds, then images taken by different cameras vary in backgrounds. To eliminate effects brought by the differences in image background, images are divided into several domains by camera Serial numbers as the first step. Then we construct undirected graphs for each camera with Euclidean distance of image pairs, and there will be an edge only when two images are close enough. The images in one maximum analogous set are regarded as the same person. Then we merge maximum cliques from different cameras simply with their cosine distance and assign pseudo labels. The proposed framework runs in an unsupervised manner, and the proposed method can obtain higher accuracy than the other related methods on Market1501 and DukeMTMC-ReID datasets, which further shows the effectiveness of the proposed method.