  Although the performance of person Re-Identification (ReID) has been
significantly boosted, many challenging issues in real scenarios have not been
fully investigated, e.g., the complex scenes and lighting variations, viewpoint
and pose changes, and the large number of identities in a camera network. To
facilitate the research towards conquering those issues, this paper contributes
a new dataset called MSMT17 with many important features, e.g., 1) the raw
videos are taken by an 15-camera network deployed in both indoor and outdoor
scenes, 2) the videos cover a long period of time and present complex lighting
variations, and 3) it contains currently the largest number of annotated
identities, i.e., 4,101 identities and 126,441 bounding boxes. We also observe
that, domain gap commonly exists between datasets, which essentially causes
severe performance drop when training and testing on different datasets. This
results in that available training data cannot be effectively leveraged for new
testing domains. To relieve the expensive costs of annotating new training
samples, we propose a Person Transfer Generative Adversarial Network (PTGAN) to
bridge the domain gap. Comprehensive experiments show that the domain gap could
be substantially narrowed-down by the PTGAN.
