计算机应用 ›› 2015, Vol. 35 ›› Issue (8): 2409-2414.DOI: 10.11772/j.issn.1001-9081.2015.08.2409

• 行业与领域应用 • 上一篇    

基于图像处理的公交车紧急状况检测

李艳艳, 吴薇   

  1. 江南大学 物联网工程学院, 江苏 无锡 214122
  • 收稿日期:2015-01-21 修回日期:2015-03-12 出版日期:2015-08-10 发布日期:2015-08-14
  • 通讯作者: 李艳艳(1990-),女,浙江宁波人,硕士研究生,主要研究方向:图形图像处理、算法设计与分析,365169372@qq.com
  • 作者简介:吴薇(1963-),男,江西南昌人,教授,博士,主要研究方向:信号处理、嵌入式系统。
  • 基金资助:

    无锡市物联网发展专项(0414B011601130052PB)。

Bus emergency detection based on image processing

LI Yanyan, WU Wei   

  1. College of Internet of Things Engineering, Jiangnan University, Wuxi Jiangsu 214122, China
  • Received:2015-01-21 Revised:2015-03-12 Online:2015-08-10 Published:2015-08-14

摘要:

针对公交车内的车载监控技术不完善且很少有紧急状况检测技术的问题,提出了一种实时检测车内紧急状况(主要体现为人的快速移动)的图像处理算法。首先,根据乘客的运动轨迹划分出人群的主要活动区域;其次,运用改进的前景提取算法提取运动前景;然后,通过Harris算子对运动前景区域提取特征点,应用光流约束的光流法对特征点建立运动矢量场;最后,通过建立KPA模型来判断是否有紧急状况发生。从理论分析和实验表明,所提算法在不同环境下检测紧急状况的成功率达83.9%以上,在实际工程应用中有实时检测的优势。

关键词: 紧急状况, 主运动区域, 前景提取, 特征点, 光流约束, KPA模型

Abstract:

To solve the problem that the vehicle monitoring technology in the bus was not perfect and few emergencies detection method were invented, a real-time detection algorithm based on image processing was proposed to detect the emergency which mainly refers to the rapid flow of the crowd in the bus. First, the main motion area was grouped according to the trajectory of the passengers. Second, an improved moving foreground extraction method was used to extract moving foreground. Then the characteristic points in the moving foreground were extracted by Harris operator, and the optical flow constraint algorithm was used to establish the motion vector filed for characteristic points. At last, the KPA (Kinetic Potential Area) model was built to recognize the emergency. Theoretical analysis and experimental results show that, in testing the emergency under different environment, the proposed algorithm has a success rate of more than 83.9%. In addition, it has advantages of real-time detection in a practical application.

Key words: emergency, main motion area, foreground extraction, feature point, optical flow constraint, KPA (Kinetic Potential Area) model

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