  Existing methods for person re-identification (Re-ID) are mostly based on
supervised learning which requires numerous manually labeled samples across all
camera views for training. Such a paradigm suffers the scalability issue since
in real-world Re-ID application, it is difficult to exhaustively label abundant
identities over multiple disjoint camera views. To this end, we propose a
progressive deep learning method for unsupervised person Re-ID in the wild by
Tracklet Association with Spatio-Temporal Regularization (TASTR). In our
approach, we first collect tracklet data within each camera by automatic person
detection and tracking. Then, an initial Re-ID model is trained based on
within-camera triplet construction for person representation learning. After
that, based on the person visual feature and spatio-temporal constraint, we
associate cross-camera tracklets to generate cross-camera triplets and update
the Re-ID model. Lastly, with the refined Re-ID model, better visual feature of
person can be extracted, which further promote the association of cross-camera
tracklets. The last two steps are iterated multiple times to progressively
upgrade the Re-ID model.
