Abstract
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
MSMT171 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