Abstract
Person re-identification (ReID) models often struggle to generalize across diverse cultural contexts, particularly in Islamic regions like Iran, where modest clothing styles are prevalent. Existing datasets predominantly feature Western and East Asian fashion, limiting their applicability in these settings. To address this gap, we introduce IUST_PersonReId, a dataset designed to reflect the unique challenges of ReID in new cultural environments, emphasizing modest attire and diverse scenarios from Iran, including markets, campuses, and mosques. Experiments on IUST_PersonReId with state-of-the-art models, such as Solider and CLIP-ReID, reveal significant performance drops compared to benchmarks like Market1501 and MSMT17, highlighting the challenges posed by occlusion and limited distinctive features. Sequence-based evaluations show improvements by leveraging temporal context, emphasizing the dataset's potential for advancing culturally sensitive and robust ReID systems. IUST_PersonReId offers a critical resource for addressing fairness and bias in ReID research globally. The dataset is publicly available at this https URL.
Abstract (translated)
人员重新识别(ReID)模型在面对不同的文化环境时经常难以泛化,尤其是在伊朗这样的伊斯兰地区,那里的服装风格通常遵循传统的保守着装规范。现有的数据集主要以西方和东亚的时尚为主,这限制了它们在这些特定场景下的适用性。为了解决这一问题,我们引入了IUST_PersonReId数据集,该数据集旨在反映新的文化环境中人员重新识别的独特挑战,特别是伊朗的各种场合,如市场、校园和清真寺中的保守着装情况。 使用最新的Solider和CLIP-ReID模型在IUST_PersonReId上的实验结果显示,与Market1501和MSMT17这样的基准数据集相比,性能有显著下降。这揭示了遮挡(occlusion)问题以及独特特征有限带来的挑战。基于序列的评估显示通过利用时间上下文可以改善识别效果,突显该数据集在推进文化敏感性和鲁棒性人员重新识别系统方面的作用。 IUST_PersonReId为解决全球范围内人员重新识别研究中的公平性和偏见提供了关键资源,并且该数据集已在[此处](https://example.com)公开发布。
URL
https://arxiv.org/abs/2412.18874