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.

本文提出了一种无监督的深度学习人物再识别方法 (UTAL), 通过联合学习内部相机移动轨迹的差异性和跨相机移动轨迹的关联性，能够从自动生成的人物运动轨迹数据中逐步发现和利用基础重识别差异性信息，实验结果表明该方法在八个基准数据集上优于当前最先进的无监督学习和领域自适应的人物再识别方法。