{
  "paper_id": "a01f9a8df47b19af5d0c852db9adb0b0",
  "paper_title": "People Counting in Crowded Environment and Re-identification",
  "paper_authors": [
    "Emanuele Frontoni",
    "Marina Paolanti",
    "Rocco Pietrini"
  ],
  "publication_date": "",
  "publication_venue": "RGB-D Image Analysis and Processing",
  "abstract": "Nowadays, detecting people and understanding their behaviour automatically is one of the key aspects of modern intelligent video systems. This interest arises from societal needs. Security and Video Analytics, Intelligent Retail Environment and Activities of Daily Living are just a few of the possible applications. The problem remains largely open due to several serious challenges such as occlusion, change of appearance, complex and dynamic background. Nevertheless, in recent years, privacy concerns are arising, making these system designs more challenging, also to cope with different worldwide country regulations. Popular sensors for this task are RGB-D cameras because of their availability, reliability and affordability. Studies have demonstrated the great value (both in accuracy and efficiency) of depth camera in coping with severe occlusions among humans and complex background. In particular, RGB-D cameras show their great potential if used in a top-view configuration achieving high performances even in a crowded environment (considering at least 3 people per square meter in the area of the camera) minimizing occlusions and also being the most privacy-compliant approach. The first step in people detection and tracking is the segmentation to retrieve people silhouette, for this reason different methods will be covered in this chapter, ranging from classical handcraft feature based approaches to deep learning techniques. These techniques also solve the nontrivial problem of blob collision, occurring when two or more people are close enough to form a unique blob from the camera point of view. Multilevel segmentation and water filling algorithms will be presented to the reader in this chapter as handcraft feature based, in addition a deep learning approach is also introduced from the literature. In the methods presented in this chapter, the elaboration occurs live (there is no image recording) and occurs on the edge, following an IoT paradigm. Live analysis also strengthens the aforementioned concept of privacy compliance. The last part of this chapter is dedicated to person re-identification (re-id), which is the process to determine if different instances or images of the same person, recorded in different moments, belong to the same subject. Person re-id has many important applications in video surveillance, because it saves human efforts on exhaustively searching for a person from large amounts of video sequences. Identification cameras are widely employed in most of the public places like malls, office buildings, airports, stations and museums. These cameras generally provide enhanced coverage and overlay large geospatial areas because they have non-overlapping fields-of-views. Huge amounts of video data, monitored in real time by law enforcement officers are used after the event for forensic purposes, are provided by these networks. An automated analysis of these data improves significantly the quality of monitoring, in addition to processing the data faster. Handcrafted anthropomorphic features coupled with a machine learning approach will be exploited in this chapter, then a deep learning approach in comparison is presented. Different metrics are then adopted to evaluate the above algorithms and to compare them.",
  "keywords": [
    "people counting",
    "re-identification",
    "RGB-D camera",
    "top-view",
    "semantic segmentation",
    "deep learning",
    "water filling",
    "multilevel segmentation"
  ],
  "paper_institutions": [],
  "output_language": "zh",
  "provider": "doubao",
  "model": "ark-code-latest",
  "prompt_template": "simple",
  "extracted_at": "2026-08-16T19:46:36.395792Z",
  "template_tag": "simple",
  "summary": "### 摘要\n本文针对拥挤环境下的人员计数和重识别问题，探讨了使用顶部视角RGB-D摄像头的优势。相关研究背景包括安全监控、智能零售和日常活动分析等应用，面临遮挡、外观变化和复杂背景等挑战，同时隐私法规也增加了设计难度。文章提出了基于深度图像的多种方法：包括背景减除、水位填充（water filling）、多级分割等传统手工特征方法，以及基于U-Net、SegNet、ResNet、FractalNet等深度学习语义分割方法。数据集方面，使用了CBSR数据集评估传统算法，TVHeads数据集评估深度学习分割，TVPR数据集用于重识别，并采集了100人的RGB-D数据。评价指标包括精确率、召回率、F1分数、Jaccard和Dice系数，以及CMC曲线。结果表明，多级分割优于水位填充，U-Net3 8位版本在分割指标上最优，重识别中结合深度和颜色特征（TVDH）效果最佳。本文还讨论了实时边缘处理和隐私合规问题，并提出了未来在音频集成和TOF传感器等方面的探索方向。\n### 关键要点\n- 顶部视角RGB-D摄像头能有效减少遮挡并保护隐私，在高度拥挤场景下计数准确率可达99%。\n- 传统背景减除方法如高斯混合模型对光照变化敏感，而结构元素方法可用于不同背景的静态图像但耗时。\n- 水位填充算法通过模拟雨滴流向局部最小值来检测头部，但遇到接近传感器的运动物体时会失效。\n- 多级分割算法能处理多人碰撞形成单一blob的情况，通过分层阈值检测每个头部。\n- 深度学习方法中，U-Net3 8位版本在TVHeads数据集上获得最佳Jaccard指数0.9625和准确率0.9945。\n- FractalNet 16位版本在精确率、召回率和F1分数上略高于U-Net3，但整体性能接近。\n- 重识别数据集TVPR包含100人，采集了7个深度特征和2个颜色直方图特征，定义TVDH描述符。\n- CMC曲线表明结合深度和颜色特征（TVDH）比单独使用TVH或TVD效果更好，深度特征在排名较高时具有优势。\n- 所有处理均在边缘实时进行，遵循IoT范例，增强隐私合规性。\n- 未来研究方向包括集成音频框架、使用TOF传感器，以及在零售场景中实时处理。"
}