摘要:近年来，随着智能监控领域的不断发展，行人重识别问题逐渐受到学术界和工业界的广泛关注，其主要研究将不同摄像头下相同身份的行人图像进行关联.当前，大部分研究工作关注在有监督场景下，即给定的训练数据都存在标记信息，然而考虑到数据标注工作的高成本，这在现实应用中往往是难以拓展的.关注于弱监督场景下的行人重识别算法，包括无监督场景和半监督场景，并且对当前先进的方法进行了分类和描述.对于无监督场景的行人重识别算法，根据其技术类型划分为5类，分别为基于伪标记的方法、基于图像生成的方法、基于实例分类的方法、基于领域自适应的方法和其他方法；对于半监督场景的行人重识别方法，根据其场景类型划分为4类，分别为少量的人有标记的场景、每一个人有少量标记的场景、基于tracklet的学习的场景和摄像头内有标记但摄像头间无标记的场景.最后，对当前行人重识别的相关数据集进行了整理，并对现有的弱监督方法的实验结果进行分析与总结.



Abstract:Recently, with the development of the intelligent surveillance, person re-identification (Re-ID) has attracted lots of attention in the academic and industrial communities, which aims to associate person images of the same identity under different non-overlapping cameras. Most of the current research works focus on the supervised case where all given training samples have label information. Considering the high cost of data labeling, these methods designed for the supervised setting have poor generalization in practical applications. This study focuses on person re-identification algorithms under the weakly supervised case including the unsupervised case and the semi-supervised case and classify and describe several state-of-the-art methods. In the unsupervised setting, these methods are divided into five categories from different technology perspectives, which include the methods based on pseudo-label, image generation, instance classification, domain adaptation, and others. In the semi-supervised setting, these methods are divided into four categories according to the case discrepancy, which are the case where a small number of persons are labeled, the case where there are few labeled images for each person, the case based on tracklet learning, and the case where there are the intra-camera labels but no inter-camera label information. Finally, several benchmark person re-identification datasets are summarized and some experimental results of these weak-supervised person re-Identification algorithms are analyzed.



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