论文标题
通过单子liDar在大规模无尺度环境中的步态识别
Gait Recognition in Large-scale Free Environment via Single LiDAR
论文作者
论文摘要
人体步态识别在多媒体中至关重要，通过步行模式实现了识别，而无需直接互动，从而增强了智能家居，医疗保健和非侵入性安全等现实世界应用中各种媒体形式的集成。激光雷达（Lidar）捕捉深度的能力使其对机器人感知的关键性至关重要，并且对现实步态识别具有希望。在本文中，基于单个激光雷尔，我们介绍了良好的步态识别的分层多代理交互网络（HMRNET）。盛行的基于LIDAR的步态数据集主要源自具有预定义轨迹的受控设置，并保持了带有现实情况的差距。为了促进基于激光雷达的步态识别研究，我们介绍了FreeGait，这是一个来自大规模，无约束设置的全面步态数据集，并具有多模式和多样化的2D/3D数据。值得注意的是，我们的方法在先前的数据集（Sustech1k）和FreeGait上实现了最先进的性能。
Human gait recognition is crucial in multimedia, enabling identification through walking patterns without direct interaction, enhancing the integration across various media forms in real-world applications like smart homes, healthcare and non-intrusive security. LiDAR's ability to capture depth makes it pivotal for robotic perception and holds promise for real-world gait recognition. In this paper, based on a single LiDAR, we present the Hierarchical Multi-representation Feature Interaction Network (HMRNet) for robust gait recognition. Prevailing LiDAR-based gait datasets primarily derive from controlled settings with predefined trajectory, remaining a gap with real-world scenarios. To facilitate LiDAR-based gait recognition research, we introduce FreeGait, a comprehensive gait dataset from large-scale, unconstrained settings, enriched with multi-modal and varied 2D/3D data. Notably, our approach achieves state-of-the-art performance on prior dataset (SUSTech1K) and on FreeGait.
