  Recently, vehicle re-identification methods based on deep learning constitute
remarkable achievement. However, this achievement requires large-scale and
well-annotated datasets. In constructing the dataset, assigning globally
available identities (Ids) to vehicles captured from a great number of cameras
is labour-intensive, because it needs to consider their subtle appearance
differences or viewpoint variations. In this paper, we propose
camera-tracklet-aware contrastive learning (CTACL) using the multi-camera
tracklet information without vehicle identity labels. The proposed CTACL
divides an unlabelled domain, i.e., entire vehicle images, into multiple
camera-level subdomains and conducts contrastive learning within and beyond the
subdomains. The positive and negative samples for contrastive learning are
defined using tracklet Ids of each camera. Additionally, the domain adaptation
across camera networks is introduced to improve the generalisation performance
of learnt representations and alleviate the performance degradation resulted
from the domain gap between the subdomains. We demonstrate the effectiveness of
our approach on video-based and image-based vehicle Re-ID datasets.
Experimental results show that the proposed method outperforms the recent
state-of-the-art unsupervised vehicle Re-ID methods. The source code for this
paper is publicly available on
`https://github.com/andreYoo/CTAM-CTACL-VVReID.git'.
