Computer Engineering and Applications ›› 2024, Vol. 60 ›› Issue (1): 40-56.DOI: 10.3778/j.issn.1002-8331.2305-0154

• Research Hotspots and Reviews • Previous Articles     Next Articles

Review of Smoking Detection Methods for Computer Vision

HE Jiabin, LI Leixiao, LIN Hao, XU Guoxin   

  1. 1.College of Data Science and Application, Inner Mongolia University of Technology, Hohhot 010080, China
    2.Inner Mongolia Autonomous Region Software Service Engineering Technology Research Center Based on Big Data, Hohhot 010080, China
    3.College of Computer Science and Engineering, Tianjin University of Technology, Tianjin 300384, China
  • Online:2024-01-01 Published:2024-01-01



  1. 1.内蒙古工业大学 数据科学与应用学院,呼和浩特 010080
    2.内蒙古自治区基于大数据的软件服务工程技术研究中心,呼和浩特 010080
    3.天津理工大学 计算机科学与工程学院,天津 300384

Abstract: Smoking in public places seriously harms people’s health and even life and property safety, so real-time and efficient smoking detection is of great significance. At present, smoking detection based on computer vision has gradually become the mainstream method with the advantages of high efficiency and high precision. On the basis of a brief overview of non-computer vision smoking detection methods, three kinds of detection methods based on computer vision are summarized. Firstly, the extraction methods of smoke features such as color, appearance and movement are discussed. Secondly, two methods of extracting cigarette target based on single step and multi-step target detection are introduced. Finally, different types of smoking action feature extraction methods are discussed from the perspectives of artificial feature construction and deep learning feature extraction. The above methods are analyzed and summarized, and the future research direction is prospected.

Key words: computer vision, smoking detection, object detection, behavior recognition

摘要: 公共场所吸烟严重危害人们身体健康甚至生命财产安全,因此实时高效的吸烟检测具有重要意义。目前基于计算机视觉的吸烟检测以高效率、高精度等优势逐渐成为主流方法。在对非计算机视觉的吸烟检测方法进行简要概述的基础上,重点归纳总结了三类基于计算机视觉的检测方法。探讨了颜色、外观、运动等多种烟雾特征的提取方法;介绍了基于单步骤和多步骤目标检测两种方法提取烟支目标;从人工特征构建、深度学习特征提取角度论述不同类型的吸烟动作特征提取方法。对上述方法进行分析总结并展望未来研究方向。

关键词: 计算机视觉, 吸烟检测, 目标检测, 行为识别