{
  "paper_id": "65f3aa71e05a66ff553283baae2d6d84",
  "paper_title": "People tracking using a Time-of-Flight depth sensor",
  "paper_authors": [
    "Alessandro Bevilacqua",
    "Luigi Di Stefano",
    "Pietro Azzari"
  ],
  "publication_date": "",
  "publication_venue": "2006 IEEE International Conference on Video and Signal Based Surveillance",
  "abstract": "Visually track several moving persons engaged in close interactions is known to be a very hard problem, though 3-D approaches based on stereo vision and plan-view maps offer much promise for dealing effectively with major issues such as occlusions and quick changes in body pose and appearance. However, in case of untextured scenes due to homogeneous objects or poor illumination, stereo-based tracking systems rapidly drop their performance. In this work, we present a real time people tracking system able to work even under severe low-lighting conditions. The system relies on a novel active sensor that provides brightness and depth images based on a Time of Flight (TOF) technology. The tracking algorithm is simple yet efficient, being based on geometrical constraints and invariants. Experiments accomplished under changing lighting conditions and involving multiple people closely interacting with each other have proved the reliability of the system.",
  "keywords": [
    "Time of Flight",
    "depth sensor",
    "people tracking",
    "multi-person tracking",
    "occlusion handling",
    "plan-view maps",
    "low-light tracking"
  ],
  "paper_institutions": [],
  "output_language": "zh",
  "provider": "doubao",
  "model": "ark-code-latest",
  "prompt_template": "simple",
  "extracted_at": "2026-08-16T19:46:11.612697Z",
  "template_tag": "simple",
  "summary": "### 摘要\n\n本文提出了一种基于飞行时间（TOF）深度传感器的实时多人跟踪系统，旨在解决传统立体视觉在无纹理场景或低光照条件下性能急剧下降的问题。该系统利用TOF传感器直接提供深度图像和亮度图像，无需进行3D场景重建，且对场景纹理和光照条件不敏感。跟踪算法基于几何约束和不变量，通过背景减除、平面视图映射（占用图和高度图）以及基于卡尔曼滤波的遮挡处理，实现多人跟踪。实验在两种室内光照条件下（正常照明和完全无外部照明）进行，场景中多达三人自由移动并发生近距离交互。结果表明，与立体相机相比，系统在正常光照下跟踪质量相当，而在无光照条件下仍能保持相同的跟踪性能，验证了该方法的可靠性和鲁棒性。核心机制是利用TOF传感器计算每个像素的深度值（d = cφ/(4πf_m)），进而构建3D点云和平面视图地图，用于检测和跟踪行人。"
}