Camera-Tracklet-Aware Contrastive Learning for Unsupervised Vehicle Re-Identification

TL;DR

This paper introduces a novel unsupervised vehicle re-identification method using camera-tracklet-aware contrastive learning that leverages multi-camera tracklet data without requiring vehicle identity labels, improving generalization across camera networks.

Contribution

It proposes a new contrastive learning framework that utilizes tracklet information and domain adaptation to enhance unsupervised vehicle re-identification performance.

Findings

Outperforms recent state-of-the-art unsupervised methods

Effective in both video-based and image-based datasets

Improves generalization across camera networks

Abstract

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'.