  Existing person re-identification (re-id) methods mostly rely on supervised
model learning from a large set of person identity labelled training data per
domain. This limits their scalability and usability in large scale deployments.
In this work, we present a novel selective tracklet learning (STL) approach
that can train discriminative person re-id models from unlabelled tracklet data
in an unsupervised manner. This avoids the tedious and costly process of
exhaustively labelling person image/tracklet true matching pairs across camera
views. Importantly, our method is particularly more robust against arbitrary
noisy data of raw tracklets therefore scalable to learning discriminative
models from unconstrained tracking data. This differs from a handful of
existing alternative methods that often assume the existence of true matches
and balanced tracklet samples per identity class. This is achieved by
formulating a data adaptive image-to-tracklet selective matching loss function
explored in a multi-camera multi-task deep learning model structure. Extensive
comparative experiments demonstrate that the proposed STL model surpasses
significantly the state-of-the-art unsupervised learning and one-shot learning
re-id methods on three large tracklet person re-id benchmarks.
