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Existing methods for person re-identification (Re-ID) are mostly based onsupervised learning which requires numerous manually labeled samples across allcamera views for training. Such a paradigm suffers the scalability issue sincein real-world Re-ID application, it is difficult to exhaustively label abundantidentities over multiple disjoint camera views. To this end, we propose aprogressive deep learning method for unsupervised person Re-ID in the wild byTracklet Association with Spatio-Temporal Regularization (TASTR). In ourapproach, we first collect tracklet data within each camera by automatic persondetection and tracking. Then, an initial Re-ID model is trained based onwithin-camera triplet construction for person representation learning. Afterthat, based on the person visual feature and spatio-temporal constraint, weassociate cross-camera tracklets to generate cross-camera triplets and updatethe Re-ID model. Lastly, with the refined Re-ID model, better visual feature ofperson can be extracted, which further promote the association of cross-cameratracklets. The last two steps are iterated multiple times to progressivelyupgrade the Re-ID model.

Progressive Unsupervised Person Re-identification by Tracklet Association with Spatio-Temporal Regularization