Camera Adversarial Transfer for Unsupervised Person Re-Identification

Unsupervised person re-identification (Re-ID) methods consist of training with a carefully labeled source dataset, followed by generalization to an unlabeled target dataset, i.e. person-identity information is unavailable. Inspired by domain adaptation techniques, these methods avoid a costly, tedious and often unaffordable labeling process. This paper investigates the use of camera-index information, namely which camera captured which image, for unsupervised person Re-ID. More precisely, inspired by domain adaptation adversarial approaches, we develop an adversarial framework in which the output of the feature extractor should be useful for person Re-ID and in the same time should fool a camera discriminator. We refer to the proposed method as camera adversarial transfer (CAT). We evaluate adversarial variants and, alongside, the camera robustness achieved for each variant. We report cross-dataset ReID performance and we compare the variants of our method with several state-of-the-art methods, thus showing the interest of exploiting camera-index information within an adversarial framework for the unsupervised person Re-ID.