Abstract
The development of deep learning has facilitated the application of person re-identification (ReID) technology in intelligent security. Visible-infrared person re-identification (VI-ReID) aims to match pedestrians across infrared and visible modality images enabling 24-hour surveillance. Current studies relying on unsupervised modality transformations as well as inefficient embedding constraints to bridge the spectral differences between infrared and visible images, however, limit their potential performance. To tackle the limitations of the above approaches, this paper introduces a simple yet effective Spectral Enhancement and Pseudo-anchor Guidance Network, named SEPG-Net. Specifically, we propose a more homogeneous spectral enhancement scheme based on frequency domain information and greyscale space, which avoids the information loss typically caused by inefficient modality transformations. Further, a Pseudo Anchor-guided Bidirectional Aggregation (PABA) loss is introduced to bridge local modality discrepancies while better preserving discriminative identity embeddings. Experimental results on two public benchmark datasets demonstrate the superior performance of SEPG-Net against other state-of-the-art methods. The code is available at this https URL.
Abstract (translated)
深度学习的发展促进了智能安全领域中人员再识别（ReID）技术的应用。可见光与红外线的人员再识别（VI-ReID）旨在跨红外和可见图像匹配行人，从而实现24小时监控。然而，当前依赖于无监督模态转换以及低效嵌入约束的方法在连接红外和可见图像之间的频谱差异时，其潜力受到了限制。为了克服上述方法的局限性，本文介绍了一种简单而有效的光谱增强与伪锚引导网络，命名为SEPG-Net。 具体来说，我们提出了一种基于频率域信息和灰度空间更均匀的光谱增强方案，该方案避免了由低效模态转换通常导致的信息损失。此外，引入了伪锚导向双向聚合（PABA）损失来弥合局部模式差异，并更好地保持具有判别性的身份嵌入。 在两个公开基准数据集上的实验结果表明，SEPG-Net优于其他最先进的方法。代码可在此URL获取：[此链接应为指向GitHub或类似平台的实际网址，在这里未提供实际链接]。
URL
https://arxiv.org/abs/2412.19111
