Camera-Tracklet-Aware Contrastive Learning for Unsupervised Vehicle Re-Identification
This addresses the labor-intensive annotation problem in vehicle re-identification for surveillance and security applications, though it is an incremental improvement over existing unsupervised methods.
The paper tackles the problem of vehicle re-identification without identity labels by proposing camera-tracklet-aware contrastive learning (CTACL), which uses multi-camera tracklet information to divide data into camera-level subdomains and perform contrastive learning, achieving state-of-the-art performance on unsupervised vehicle Re-ID datasets.
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'.