A Novel Method for Person Re-Identification: Conditional Translated Network Based on GANs

The main challenge of person re-identification (re-id) lies in the strikingly discrepancy between different camera views, including illumination, background and human pose. Existing person re-id methods rely mostly on implicit solutions, such as seeking robust features or designing discriminative distance metrics. Compared to these methods, human solutions are more straightforward. That is, imagine the appearance of the target person under different camera views before matching target person. The key idea is that human can intuitively implement viewpoint transfer, noting the association of the target person under different camera views but the machine failed. In this paper, we attempt to imitate such human behavior that transfer person image to certain camera views before matching. In practice, we propose a conditional transfer network (cTransNet) that conditionally implement viewpoint transfer, which transfers image to the viewpoint with the biggest domain gap through a variant of Generative Adversarial Networks (GANs). After that, we obtain hybrid person representation by fusing the feature of original image with the transferred image then perform similarity ranking according to cosine distance. Compared with former methods, we propose a human-like approach and obtains consistent improvement of the rank-1 precision over the baseline in Market-1501, DukeMTMC-ReID and MSMT17 dataset by 3%,4%,4%, respectively.