Camera Alignment and Weighted Contrastive Learning for Domain Adaptation in Video Person ReID

TL;DR

This paper introduces a novel unsupervised domain adaptation method for video person re-identification that uses camera alignment and weighted contrastive learning to improve clustering and model accuracy across diverse camera networks.

Contribution

It proposes a camera-discriminator network for extracting camera-independent features and a weighted contrastive loss to enhance clustering confidence in unsupervised video ReID.

Findings

Outperforms state-of-the-art methods on PRID2011, iLIDS-VID, and MARS datasets.

Effectively mitigates domain shift across different camera views.

Improves clustering accuracy and ReID performance in unsupervised settings.

Abstract

Systems for person re-identification (ReID) can achieve a high accuracy when trained on large fully-labeled image datasets. However, the domain shift typically associated with diverse operational capture conditions (e.g., camera viewpoints and lighting) may translate to a significant decline in performance. This paper focuses on unsupervised domain adaptation (UDA) for video-based ReID - a relevant scenario that is less explored in the literature. In this scenario, the ReID model must adapt to a complex target domain defined by a network of diverse video cameras based on tracklet information. State-of-art methods cluster unlabeled target data, yet domain shifts across target cameras (sub-domains) can lead to poor initialization of clustering methods that propagates noise across epochs, thus preventing the ReID model to accurately associate samples of same identity. In this paper, an UDA method is introduced for video person ReID that leverages knowledge on video tracklets, and on the distribution of frames captured over target cameras to improve the performance of CNN backbones trained using pseudo-labels. Our method relies on an adversarial approach, where a camera-discriminator network is introduced to extract discriminant camera-independent representations, facilitating the subsequent clustering. In addition, a weighted contrastive loss is proposed to leverage the confidence of clusters, and mitigate the risk of incorrect identity associations. Experimental results obtained on three challenging video-based person ReID datasets - PRID2011, iLIDS-VID, and MARS - indicate that our proposed method can outperform related state-of-the-art methods. Our code is available at: \url{https://github.com/dmekhazni/CAWCL-ReID}