{
  "paper_id": "123e22daaf6ec7858e0dd7d110709d73",
  "paper_title": "A robust people detection, tracking, and counting system",
  "paper_authors": [
    "Nathan Kirchner",
    "Alen Alempijevic",
    "Alexander Virgona",
    "Xiaoke Dai",
    "Paul G. Plöger",
    "Ravi Kumar Venkat"
  ],
  "publication_date": "",
  "publication_venue": "unknown",
  "abstract": "The ability to track moving people is a key aspect of autonomous robot systems in real-world environments. Whilst for many tasks knowing the approximate positions of people may be sufficient, the ability to identify unique people is needed to accurately count people in the real world. To accomplish the people counting task, a robust system for people detection, tracking and identification is needed. This paper presents our approach for robust real world people detection, tracking and counting using a PrimeSense RGBD camera. Our past research, upon which we built, is highlighted and novel methods to solve the problems of sensor self-localisation, false negatives due to persons physically interacting with the environment, and track misassociation due to crowdedness are presented. An empirical evaluation of our approach in a major Sydney public train station (N=420) was conducted, and results demonstrating our methods in the complexities of this challenging environment are presented.",
  "keywords": [],
  "paper_institutions": [
    "Centre for Autonomous Systems, University of Technology, Sydney",
    "Institute of Cyber-Systems and Control, Zhejiang University",
    "Bonn-Rhein-Sieg University of Applied Science"
  ],
  "output_language": "zh",
  "provider": "doubao",
  "model": "ark-code-latest",
  "prompt_template": "simple",
  "extracted_at": "2026-08-16T19:46:46.246607Z",
  "template_tag": "simple",
  "summary": "### 摘要\n本文针对真实世界中机器人系统对行人检测、跟踪与计数的需求，提出了一种基于PrimeSense RGBD相机的鲁棒系统。现有方法在传感器定位、人员与环境交互导致的漏检、以及拥挤场景下轨迹误关联等方面存在局限。系统在先前研究基础上，新增了传感器自定位、利用静态图像侧链消除环境交互干扰、以及基于头肩签名（HSS）的轨迹关联方法。在悉尼主要火车站进行了实证评估（N=420），结果显示系统无假阳性计数，能有效处理拥挤、人员与环境接触及合并/分离事件，验证了方法的鲁棒性和实际可行性。\n\n### 关键要点\n- 系统由自定位、人员检测、人员跟踪和人员计数四个阶段组成，整体框架应对真实世界挑战。\n- 提出基于区域增长的地面检测方法，从点云中提取最大平面，计算传感器的俯仰角、翻滚角和高度，实现自动自定位。\n- 利用静态背景图像减去环境因素（如扶手），解决人员与设施物理接触导致的检测漏检问题。\n- 人员检测采用三步流程：点云分割、特征向量构建（HSS）和支持向量机分类，HSS对360度视角具有鲁棒性。\n- 粒子滤波器用于多目标跟踪，每个目标独立粒子滤波器，预测模型采用高斯随机运动模型（均值1.4m/s，标准差0.5）。\n- 轨迹初始化基于红色检测级别，验证需要绿色级别（HSS分类通过），删除条件为粒子滤波器不确定性超过0.2m。\n- 合并/分离事件通过个人空间距离（0.8m）触发，使用HSS残差和（每片<4mm）进行身份再关联。\n- 在悉尼火车站六个位置（四个站台、两个大厅）采集深度图像，每个位置两个时段，覆盖稀疏至拥挤客流。\n- 人工逐帧验证密集场景（最多9人同时出现），系统即时人数与人工计数一致，验证了拥挤处理能力。\n- 总计数实验：人工420人，软件372人，误检0人，漏检48人；漏检主因是视野边缘严重截断导致HSS特征畸形。\n- 系统有效检测距离为4米，超出部分被排除；未来工作将识别同一人再次出现，生成唯一人员计数。",
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}