In person re-identification (Re-ID), supervised methods usually need a large amount of expensive label information, while unsupervised ones are still unable to deliver satisfactory identification performance. In this paper, we introduce a novel person Re-ID task called unsupervised cross-camera person Re-ID, which only needs the within-camera (intra-camera) label information but not cross-camera (inter-camera) labels which are more expensive to obtain. In real-world applications, the intra-camera label information can be easily captured by tracking algorithms or few manual annotations. In this situation, the main challenge becomes the distribution discrepancy across different camera views, caused by the various body pose, occlusion, image resolution, illumination conditions, and background noises in different cameras. To address this situation, we propose a novel Adversarial Camera Alignment Network (ACAN) for unsupervised cross-camera person Re-ID. It consists of the camera-alignment task and the supervised within-camera learning task. To achieve the camera alignment, we develop a Multi-Camera Adversarial Learning (MCAL) to map images of different cameras into a shared subspace. Particularly, we investigate two different schemes, including the existing GRL (i.e., gradient reversal layer) scheme and the proposed scheme called "other camera equiprobability" (OCE), to conduct the multi-camera adversarial task. Based on this shared subspace, we then leverage the within-camera labels to train the network. Extensive experiments on five large-scale datasets demonstrate the superiority of ACAN over multiple state-of-the-art unsupervised methods that take advantage of labeled source domains and generated images by GAN-based models. In particular, we verify that the proposed multi-camera adversarial task does contribute to the significant improvement.

翻译：在个人再识别(Re-ID)中,受监督的方法通常需要大量昂贵的标签信息,而不受监督的标签信息仍然无法提供令人满意的身份识别性能。在本文中,我们引入了一个新的人再识别任务,名为“不受监督的跨相机人重新识别”,它只需要闭路电视(内摄像机)标签信息,而不是要更昂贵才能获得的跨相机(内摄像机)标签。在现实世界应用程序中,通过跟踪算法或很少的手动说明可以很容易地获取闭路电视内部的标签信息。在这种情况下,主要的挑战就变成了不同摄像头对不同摄像头的分布差异。为了解决这一问题,我们建议建立一个新型Aversari相机(内摄像头)标签(内比对摄像头更昂贵 ), 由摄像头内部的对比任务和监控内部学习任务。为了实现摄像头对非摄像头的调整,我们开发多摄像头AAAAdversarial 数据流的分布差异的图像, 包括我们内部的图像 。