{
  "paper_id": "e191c63047bb9653a977bfda6fb134e6",
  "paper_title": "LIDAR TECHNOLOGY FOR HUMAN ACTIVITY RECOGNITION: OUTLOOKS AND CHALLENGES",
  "paper_authors": [
    "Omar Rinchi",
    "Hakim Ghazzai",
    "Ahmad Alsharoa",
    "Yehia Massoud"
  ],
  "publication_date": "",
  "publication_venue": "IEEE Internet of Things Magazine",
  "abstract": "The smart and autonomous learning and recognition of human activities will certainly lead to an incredible progression for several applications and services in public healthcare, education, entertainment, safety and security, and more. With the recent advances in artificial intelligence, signal processing, and computational capabilities, light detection and ranging (LiDAR) technology can play an instrumental role to revamp current human activity recognition (HAR) systems. In this magazine, we investigate the potential of using LiDAR sensors as a novel technology enabling complex and real-time HAR applications. We first overview the HAR categories and the existing state-of-the-art technologies: video-based cameras, depth sensors, wearable devices, and WiFi. Afterward, we delve into the integration of LiDAR technology to perform HAR applications. Then, we discuss the advantages and drawbacks of utilizing LiDAR technology for HAR and compare it with the existing techniques. Finally, we discuss the challenges that need to be addressed to enable advanced LiDAR-based HAR applications.",
  "keywords": [
    "LiDAR",
    "human activity recognition",
    "HAR",
    "point cloud",
    "deep learning",
    "sensor fusion"
  ],
  "paper_institutions": [
    "Missouri University of Science and Technology",
    "King Abdullah University of Science and Technology"
  ],
  "output_language": "zh",
  "provider": "doubao",
  "model": "ark-code-latest",
  "prompt_template": "simple",
  "extracted_at": "2026-08-16T19:47:17.467421Z",
  "template_tag": "simple",
  "summary": "### 摘要\n本文探讨了使用LiDAR技术进行人类活动识别（HAR）的潜力和挑战。首先概述了HAR的分类和现有技术（视频摄像头、深度传感器、可穿戴设备和WiFi）的优缺点，然后重点分析了LiDAR在个人、群体和人群活动识别中的适用性。LiDAR通过生成3D点云提供深度信息，不受光照影响，且避免了采集个人身份信息（PII），在隐私保护方面具有优势。文中对比了LiDAR与现有技术，展示了其在黑暗环境、烟尘检测等方面的独特能力。在实现上，LiDAR数据处理涉及预处理（如ROI过滤、聚类）、学习模型（如PointNet、VoxelNet、PV-RCNN）和后处理（如跟踪）。尽管现有研究已展示初步成果（如利用2D LiDAR进行跌倒检测），但LiDAR-based HAR仍面临数据获取困难、模型复杂度高、多传感器融合、环境干扰等问题。作者建议利用仿真工具（如Webots）生成虚拟点云数据，并结合边缘计算和降维技术应对数据处理挑战。总体而言，LiDAR技术有望成为实时、隐私保护的HAR解决方案，但需进一步研究以克服现有局限。\n\n### 关键要点\n- LiDAR技术通过主动发射激光生成3D点云，能够提供深度和反射率信息，不受光照变化影响，适用于夜间监控。\n- 与视频摄像头相比，LiDAR不采集传统图像，避免识别面部等个人身份信息，有效保护隐私。\n- LiDAR适用于个体、群体和人群活动识别，可通过分析点云趋势识别异常行为，如人群恐慌或暴力事件。\n- 现有HAR技术包括视频、深度传感器、可穿戴设备和WiFi，各有缺陷：视频受隐私和光照限制，深度传感器受限于室内和距离，可穿戴设备需用户佩戴，WiFi受多径和环境影响。\n- LiDAR数据预处理包括ROI过滤、DBSCAN聚类、旋转矩阵变换、标注和数据增强，以提升后续学习模型效果。\n- 点云学习模型分为基于点（如PointNet）、基于体素（如VoxelNet）和混合方法（如PV-RCNN），各有优缺点。\n- 现有LiDAR-based HAR研究较少，部分工作利用2D LiDAR结合CNN和RNN进行跌倒检测，或融合RGB和3D LiDAR实现九种动作识别，准确率超90%。\n- 多LiDAR系统可覆盖更广区域，但需要解决数据同步、融合（点云级、特征级、决策级）和传感器布局优化问题。\n- MEC（移动边缘计算）可降低云端数据传输瓶颈，平衡处理速度与网络负载，适合实时HAR应用。\n- 高维数据处理可采用特征选择（如随机森林）或空间变换（如PCA）技术，也可利用稀疏恢复压缩数据。\n- LiDAR与其他传感器（如相机、可穿戴设备）融合可提高可靠性，但需设计有效的融合策略，现有融合主要针对目标检测。\n- 环境因素（雨、雪、雾）会衰减LiDAR信号，可通过算法噪声抑制、改进硬件（如FMCW）或传感器融合来缓解。"
}