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.

翻译：长期行人重识别（LRe-ID）旨在经过长时间跨度后跨摄像头匹配同一行人，在此过程中行人的衣物、姿态和视角可能发生显著变化。本文提出CCPA：面向LRe-ID的对比式衣物与姿态增强框架。除外观特征外，CCPA通过关系图注意力网络捕捉与衣物无关的体型信息。训练鲁棒的LRe-ID模型需要多样的衣物变化及昂贵的衣物标注，而现有LRe-ID数据集缺乏此类资源。为此，我们跨身份执行衣物与姿态迁移，生成更多衣物变化以及不同行人穿着相似衣物的图像。增强后的图像批次作为输入，送入我们提出的细粒度对比损失函数，该损失不仅监督重识别模型学习长期场景下具有判别力的行人嵌入，还能确保数据生成处于分布内。在LRe-ID数据集上的实验结果验证了CCPA框架的有效性。