QAGait：从质量视角重新审视步态识别
QAGait: Revisit Gait Recognition from a Quality Perspective
AI总结：
本文提出QAGait，从轮廓质量视角重构步态识别流程，通过质量评估策略和质量感知损失提升野外场景下的可靠性与识别性能。
摘要：
步态识别是一种很有前景的生物识别方法，旨在根据行人独特的行走模式识别其身份。轮廓模态因易于获取、结构简单、表示稀疏且建模方便而闻名，已被广泛用于受控的实验室研究。然而，随着步态识别从实验室快速走向野外场景，多种条件给轮廓模态带来了重大挑战，包括：1）无法识别的低质量轮廓（分割异常、严重遮挡，甚至非人形），以及2）可识别但具有挑战性的轮廓（背景噪声、非标准姿态、轻微遮挡）。为应对这些挑战，我们重新审视步态识别流程，并从质量视角处理步态识别，即QAGait。具体而言，我们提出了一系列高性价比的质量评估策略，包括用于消除背景噪声和不可识别轮廓的Maxmial Connect Area与Template Match，以及用于处理非标准姿态的Alignment策略。我们还提出了两个质量感知损失函数，以在嵌入空间的优化中整合轮廓质量。大量实验表明，我们的QAGait能够同时保证步态可靠性和性能提升。此外，我们的质量评估策略可与现有步态数据集无缝集成，展现了我们的优越性。代码可在https://github.com/wzb-bupt/QAGait获取。
英文摘要：
Gait recognition is a promising biometric method that aims to identify pedestrians from their unique walking patterns. Silhouette modality, renowned for its easy acquisition, simple structure, sparse representation, and convenient modeling, has been widely employed in controlled in-the-lab research. However, as gait recognition rapidly advances from in-the-lab to in-the-wild scenarios, various conditions raise significant challenges for silhouette modality, including 1) unidentifiable low-quality silhouettes (abnormal segmentation, severe occlusion, or even non-human shape), and 2) identifiable but challenging silhouettes (background noise, non-standard posture, slight occlusion). To address these challenges, we revisit gait recognition pipeline and approach gait recognition from a quality perspective, namely QAGait. Specifically, we propose a series of cost-effective quality assessment strategies, including Maxmial Connect Area and Template Match to eliminate background noises and unidentifiable silhouettes, Alignment strategy to handle non-standard postures. We also propose two quality-aware loss functions to integrate silhouette quality into optimization within the embedding space. Extensive experiments demonstrate our QAGait can guarantee both gait reliability and performance enhancement. Furthermore, our quality assessment strategies can seamlessly integrate with existing gait datasets, showcasing our superiority. Code is available at https://github.com/wzb-bupt/QAGait.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
