CCPA: Long-term Person Re-Identification via Contrastive Clothing and Pose Augmentation
This work addresses the challenge of matching individuals across cameras over long periods, which is important for surveillance and security applications, but it is incremental as it builds on existing methods with specific enhancements.
The paper tackles the problem of long-term person re-identification by proposing a framework that uses contrastive clothing and pose augmentation to generate more clothing variations and learn discriminative embeddings, achieving improved results on LRe-ID datasets.
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.