  Supervised person re-identification methods rely heavily on high-quality
cross-camera training label. This significantly hinders the deployment of re-ID
models in real-world applications. The unsupervised person re-ID methods can
reduce the cost of data annotation, but their performance is still far lower
than the supervised ones. In this paper, we make full use of the auxiliary
information mined from the datasets for multi-modal feature learning, including
camera information, temporal information and spatial information. By analyzing
the style bias of cameras, the characteristics of pedestrians' motion
trajectories and the positions of camera network, this paper designs three
modules: Time-Overlapping Constraint (TOC), Spatio-Temporal Similarity (STS)
and Same-Camera Penalty (SCP) to exploit the auxiliary information. Auxiliary
information can improve the model performance and inference accuracy by
constructing association constraints or fusing with visual features. In
addition, this paper proposes three effective training tricks, including
Restricted Label Smoothing Cross Entropy Loss (RLSCE), Weight Adaptive Triplet
Loss (WATL) and Dynamic Training Iterations (DTI). The tricks achieve mAP of
72.4% and 81.1% on MARS and DukeMTMC-VideoReID, respectively. Combined with
auxiliary information exploiting modules, our methods achieve mAP of 89.9% on
DukeMTMC, where TOC, STS and SCP all contributed considerable performance
improvements. The method proposed by this paper outperforms most existing
unsupervised re-ID methods and narrows the gap between unsupervised and
supervised re-ID methods. Our code is at
https://github.com/tenghehan/AuxUSLReID.
