Unsupervised Person Re-identification by Deep Learning Tracklet Association

Minxian Li, Xiatian Zhu, Shaogang Gong; The European Conference on Computer Vision (ECCV), 2018, pp. 737-753

Most existing person re-identification (re-id) methods rely on supervised model learning on per-camera-pair manually labelled pairwise training data. This leads to poor scalability in practical re-id deployment due to the lack of exhaustive identity (ID) labelling of image pairs (both positive and negative) for every camera pair. In this work, we address this problem by proposing an unsupervised re-id deep learning approach capable of incrementally discovering and exploiting the underlying re-id discriminative information from automatically generated person tracklet data from videos in an end-to-end deep model optimisation. We formulate a Tracklet Association Unsupervised Deep Learning (TAUDL) framework characterised by jointly learning per-camera (within-camera) tracklet association (labelling) and cross-camera tracklet correlation by maximising the discovery of most likely tracklet relationships across camera views without cross-view pairwise person identity labelling. Extensive experiments demonstrate the superiority of the proposed TAUDL model over the state-of-the-art unsupervised and domain adaptation re-id methods using six person re-id benchmarking datasets.