Discriminative Pedestrian Features and Gated Channel Attention for Clothes-Changing Person Re-Identification

TL;DR

This paper presents a novel approach for clothes-changing person re-identification that uses disentangled feature extraction and gated channel attention to improve identity recognition despite clothing variations.

Contribution

It introduces a new method combining disentangled features and gated channel attention to enhance clothes-invariant person re-identification.

Findings

Achieved Top-1 accuracy of 64.8% on PRCC dataset.

Achieved Top-1 accuracy of 83.7% on VC-Clothes dataset.

Outperformed existing methods on standard CC-ReID datasets.

Abstract

In public safety and social life, the task of Clothes-Changing Person Re-Identification (CC-ReID) has become increasingly significant. However, this task faces considerable challenges due to appearance changes caused by clothing alterations. Addressing this issue, this paper proposes an innovative method for disentangled feature extraction, effectively extracting discriminative features from pedestrian images that are invariant to clothing. This method leverages pedestrian parsing techniques to identify and retain features closely associated with individual identity while disregarding the variable nature of clothing attributes. Furthermore, this study introduces a gated channel attention mechanism, which, by adjusting the network's focus, aids the model in more effectively learning and emphasizing features critical for pedestrian identity recognition. Extensive experiments conducted on two standard CC-ReID datasets validate the effectiveness of the proposed approach, with performance surpassing current leading solutions. The Top-1 accuracy under clothing change scenarios on the PRCC and VC-Clothes datasets reached 64.8% and 83.7%, respectively.