行走更远：基于语义的多模态步态识别在远距离条件下的应用
Walking Further: Semantic-aware Multimodal Gait Recognition Under Long-Range Conditions
AI总结：
本文提出LRGait基准和EMGaitNet框架，通过语义引导融合和跨模态对齐提升远距离步态识别性能，验证了方法的有效性。
摘要：
步态识别是一种新兴的生物识别技术，能够实现非侵入性和难以伪造的人类身份验证。然而，现有方法大多局限于短距离单模态设置，无法在现实条件下推广到远距离和跨距离场景。为解决这一差距，我们提出了LRGait，这是首个针对稳健远距离步态识别的LiDAR-相机多模态基准，适用于多种户外距离和环境。我们进一步提出了EMGaitNet，一个端到端框架，专门用于远距离多模态步态识别。为了弥合RGB图像和点云之间的模态差距，我们引入了语义引导的融合流程。一个基于CLIP的语义挖掘（SeMi）模块首先提取人体部位感知的语义线索，然后通过语义引导对齐（SGA）模块在统一嵌入空间中对齐2D和3D特征。对称交叉注意力融合（SCAF）模块分层整合视觉轮廓和3D几何特征，而时空（ST）模块捕捉全局步态动态。在各种步态数据集上的广泛实验验证了我们方法的有效性。
英文摘要：
Gait recognition is an emerging biometric technology that enables non-intrusive and hard-to-spoof human identification. However, most existing methods are confined to short-range, unimodal settings and fail to generalize to long-range and cross-distance scenarios under real-world conditions. To address this gap, we present \textbf{LRGait}, the first LiDAR-Camera multimodal benchmark designed for robust long-range gait recognition across diverse outdoor distances and environments. We further propose \textbf{EMGaitNet}, an end-to-end framework tailored for long-range multimodal gait recognition. To bridge the modality gap between RGB images and point clouds, we introduce a semantic-guided fusion pipeline. A CLIP-based Semantic Mining (SeMi) module first extracts human body-part-aware semantic cues, which are then employed to align 2D and 3D features via a Semantic-Guided Alignment (SGA) module within a unified embedding space. A Symmetric Cross-Attention Fusion (SCAF) module hierarchically integrates visual contours and 3D geometric features, and a Spatio-Temporal (ST) module captures global gait dynamics. Extensive experiments on various gait datasets validate the effectiveness of our method.
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。
