Pseudo Labels Refinement with Intra-camera Similarity for Unsupervised Person Re-identification
This work improves unsupervised person re-identification for surveillance and security applications, but it is incremental as it builds on existing clustering-based methods.
The paper tackles the problem of unsupervised person re-identification by addressing feature distribution noise from domain shifts across cameras, proposing a label refinement framework using intra-camera similarity to improve pseudo label reliability, resulting in state-of-the-art performance.
Unsupervised person re-identification (Re-ID) aims to retrieve person images across cameras without any identity labels. Most clustering-based methods roughly divide image features into clusters and neglect the feature distribution noise caused by domain shifts among different cameras, leading to inevitable performance degradation. To address this challenge, we propose a novel label refinement framework with clustering intra-camera similarity. Intra-camera feature distribution pays more attention to the appearance of pedestrians and labels are more reliable. We conduct intra-camera training to get local clusters in each camera, respectively, and refine inter-camera clusters with local results. We hence train the Re-ID model with refined reliable pseudo labels in a self-paced way. Extensive experiments demonstrate that the proposed method surpasses state-of-the-art performance.