  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 a practical re-id deployment, due to the lack
of exhaustive identity labelling of positive and negative image pairs for every
camera-pair. In this work, we present an unsupervised re-id deep learning
approach. It is capable of incrementally discovering and exploiting the
underlying re-id discriminative information from automatically generated person
tracklet data end-to-end. We formulate an Unsupervised Tracklet Association
Learning (UTAL) framework. This is by jointly learning within-camera tracklet
discrimination and cross-camera tracklet association in order to maximise the
discovery of tracklet identity matching both within and across camera views.
Extensive experiments demonstrate the superiority of the proposed model over
the state-of-the-art unsupervised learning and domain adaptation person re-id
methods on eight benchmarking datasets.
