  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.
