CCPA: Long-term Person Re-Identification via Contrastive Clothing and Pose Augmentation

TL;DR

This paper introduces CCPA, a framework for long-term person re-identification that leverages clothing and pose augmentation, body shape modeling, and contrastive learning to improve matching accuracy over time despite clothing and pose variations.

Contribution

CCPA is the first to combine clothing and pose transfer with contrastive learning for long-term person re-identification, capturing cloth-invariant body shape features.

Findings

CCPA outperforms existing methods on LRe-ID datasets.

Clothing and pose augmentation improve model robustness.

Contrastive losses enhance discriminative embedding learning.

Abstract

Long-term Person Re-Identification (LRe-ID) aims at matching an individual across cameras after a long period of time, presenting variations in clothing, pose, and viewpoint. In this work, we propose CCPA: Contrastive Clothing and Pose Augmentation framework for LRe-ID. Beyond appearance, CCPA captures body shape information which is cloth-invariant using a Relation Graph Attention Network. Training a robust LRe-ID model requires a wide range of clothing variations and expensive cloth labeling, which is lacked in current LRe-ID datasets. To address this, we perform clothing and pose transfer across identities to generate images of more clothing variations and of different persons wearing similar clothing. The augmented batch of images serve as inputs to our proposed Fine-grained Contrastive Losses, which not only supervise the Re-ID model to learn discriminative person embeddings under long-term scenarios but also ensure in-distribution data generation. Results on LRe-ID datasets demonstrate the effectiveness of our CCPA framework.