Unsupervised Tracklet Person Re-Identification
This addresses the need for scalable person re-identification without manual labeling, though it is incremental as it builds on existing unsupervised and domain adaptation methods.
The paper tackles the scalability problem in person re-identification by proposing an unsupervised deep learning approach that incrementally discovers discriminative information from automatically generated tracklet data, achieving state-of-the-art results on eight benchmarking datasets.
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.