Abstract
Clothes-Changing Person Re-Identification (ReID) aims to recognize the same individual across different videos captured at various times and locations. This task is particularly challenging due to changes in appearance, such as clothing, hairstyle, and accessories. We propose a Clothes-Changing ReID method that uses only skeleton data and does not use appearance features. Traditional ReID methods often depend on appearance features, leading to decreased accuracy when clothing changes. Our approach utilizes a spatio-temporal Graph Convolution Network (GCN) encoder to generate a skeleton-based descriptor for each individual. During testing, we improve accuracy by aggregating predictions from multiple segments of a video clip. Evaluated on the CCVID dataset with several different pose estimation models, our method achieves state-of-the-art performance, offering a robust and efficient solution for Clothes-Changing ReID.
Abstract (translated)
服装变化下的重识别（ReID）旨在跨不同时间和地点捕捉的视频中识别同一人。这一任务特别具有挑战性，因为人的外貌会随时间发生变化，例如穿着、发型和配饰等。我们提出了一种仅使用骨架数据而不依赖于外观特征的服装变化下的人再识别方法。传统的人再识别方法通常依靠外观特征，在遇到着装变化时准确率会降低。 我们的方法利用了时空图卷积网络（GCN）编码器，为每个人生成基于骨架描述符。在测试阶段，我们通过汇集视频片段多个时间段上的预测来提高准确性。我们在使用多种姿态估计模型的CCVID数据集上进行了评估，取得了最先进的性能，提供了一种鲁棒且高效的人再识别解决方案，在服装变化情况下也能保持高精度。
URL
https://arxiv.org/abs/2503.10759
