解决衣物变化时发型干扰导致的行人重识别难题
MSP-ReID: Hairstyle-Robust Cloth-Changing Person Re-Identification
- 分离面部与发型，用增广增强身份一致性
- 保留衣物结构信息，抑制纹理偏差影响
- 适合长期监控场景下的跨摄像头识别
衣物变化行人重识别（CC-ReID）旨在不同服装条件下跨摄像头匹配同一人。现有方法常移除衣物并聚焦头部以减少服装偏差，但将头部整体处理而不区分面部与发型，会导致对易变发型特征的过度依赖，进而引发发型变化时性能下降。为此，我们提出缓解发型干扰与结构保持（MSP）框架。MSP引入发型导向增广（HSOA），生成同一身份的发型多样性，降低对发型的依赖，强化对稳定面部与躯体特征的关注。为防止结构信息丢失，设计衣物保留随机擦除（CPRE），在衣物区域进行比例可控擦除，抑制纹理偏差同时保留身体形状与上下文。此外，采用基于区域解析注意力（RPA），融合解析引导先验，突出面部与肢体区域，抑制头发特征。在多个CC-ReID基准上的大量实验表明，MSP达到当前最优性能，为长期行人重识别提供了鲁棒且实用的解决方案。
原文摘要 · Abstract (English)
Cloth-Changing Person Re-Identification (CC-ReID) aims to match the same individual across cameras under varying clothing conditions. Existing approaches often remove apparel and focus on the head region to reduce clothing bias. However, treating the head holistically without distinguishing between face and hair leads to over-reliance on volatile hairstyle cues, causing performance degradation under hairstyle changes. To address this issue, we propose the Mitigating Hairstyle Distraction and Structural Preservation (MSP) framework. Specifically, MSP introduces Hairstyle-Oriented Augmentation (HSOA), which generates intra-identity hairstyle diversity to reduce hairstyle dependence and enhance attention to stable facial and body cues. To prevent the loss of structural information, we design Cloth-Preserved Random Erasing (CPRE), which performs ratio-controlled erasing within clothing regions to suppress texture bias while retaining body shape and context. Furthermore, we employ Region-based Parsing Attention (RPA) to incorporate parsing-guided priors that highlight face and limb regions while suppressing hair features. Extensive experiments on multiple CC-ReID benchmarks demonstrate that MSP achieves state-of-the-art performance, providing a robust and practical solution for long-term person re-identification.
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