{
  "paper_id": "0d9ad4a711ce532ba0e00dd7693d0612",
  "paper_title": "Approach Based on Geometric Shape of Pedestrian's Head to Shoulder Region for Human Tracking in High Density Crowd Using a 3D Laser Range Finder",
  "paper_authors": [
    "Yuta Sampei",
    "Mihoko Niitsuma"
  ],
  "publication_date": "",
  "publication_venue": "2017 14th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI)",
  "abstract": "This paper discusses the possibility of using the geometric shape of a pedestrian’s head to shoulder region as a feature to identify individuals. The main aim is to track humans in a high density crowd using a 3D laser range finder (3D-LRF). One of the most critical problems of human tracking in a high density crowd is the occurrence of occlusion. Occlusion might occur because of the lack of features on tracking objects and the fragmentation of trajectories. When the trajectories are fragmented, they should be associated correctly in order to realize robust and accurate tracking. To improve the robustness of human tracking in a crowded environment, we consider features of a tracking object that can recognize individuals to be useful when combining trajectories fragmented by occlusion. In this paper, the geometric shape of a pedestrian’s head to shoulder region was applied as a feature of a tracking object. The applicability of the feature to the tracking in dynamic environments was done through experiments using a 3D-LRF.",
  "keywords": [
    "human tracking",
    "3D-LRF",
    "geometrical feature"
  ],
  "paper_institutions": [
    "Chuo University"
  ],
  "output_language": "zh",
  "provider": "doubao",
  "model": "ark-code-latest",
  "prompt_template": "simple",
  "extracted_at": "2026-08-16T19:47:15.799870Z",
  "template_tag": "simple",
  "summary": "### 摘要\n本文旨在解决高密度人群中使用三维激光测距仪（3D-LRF）进行人体跟踪时因遮挡导致的轨迹碎片化问题。相关研究背景包括基于二维激光测距仪的跟踪方法在遮挡场景下的局限性，以及头肩签名（HSS）在人员识别中的潜力。提出利用行人头部至肩部区域的几何形状作为个体识别特征，以辅助遮挡后轨迹的关联。实验采用UTM-30LX型3D-LRF（水平视场宽、点云密度不同于Kinect），该传感器安全性符合Class 1标准，适用于动态环境。实验设置中传感器距地面960毫米，将头肩区域按5个水平切片构造HSS，比较静止与行走状态下各切片跨度。结果表明，行走时跨度变化较大，尤其是切片4因肩部是否被包含而波动显著，说明HSS依赖于传感器与人体的相对位置关系。未来工作将基于相对位置关系改进HSS计算方法并应用于实际跟踪。\n### 关键要点\n- 高密度人群中人眼跟踪的主要挑战是遮挡导致的轨迹碎片化，需正确关联碎片化轨迹以实现鲁棒跟踪。\n- 提出使用行人头部到肩部的几何形状（HSS）作为个体识别特征，因为其遮挡概率较低且能单独依赖个体差异。\n- 采用3D-LRF（UTM-30LX）替代Kinect，因其水平视场更宽、点云密度不同，且激光安全等级为Class 1，适合动态大范围环境。\n- 人员提取方法基于Uenoyama等人的背景相减法：通过测量距离直方图确定背景，再减去背景检测移动物体。\n- HSS构造：取头肩区域40厘米点云，分割为等厚水平切片，计算每片最大跨度，将跨度序列作为特征。\n- 实验中传感器安装高度960毫米，确保肩部以上区域始终进入扫描范围，切片数设为5以匹配垂直角度分辨率。\n- 对比静止（3米处）与行走时各切片跨度，静止时跨度均值与手动测量接近，但行走时差异较大。\n- 行走时切片4的跨度标准差最大（75.8毫米），因为肩部是否包含在切片内导致数据混合。\n- 结果表明HSS不仅依赖个体，还受传感器与人体相对位置和姿态影响，行走时身体朝向变化引起遮挡。\n- 传统二维LRF在三人以上高密度场景难以精确跟踪，而3D-LRF可提供三维信息，但HSS仍需改进。\n- 未来计划基于相对位置关系重新计算HSS，并应用于高密度人群跟踪。\n- 实验验证了HSS在动态环境中的适用性有限，需进一步考虑相对关系以稳定提取特征。"
}