华南理工大学学报(自然科学版) ›› 2015, Vol. 43 ›› Issue (4): 1-6.doi: 10.3969/j.issn.1000-565X.2015.04.001

• 交通与运输工程 •    下一篇

基于背景差分的高速公路运动目标检测算法

符锌砂1 王祥波1 李海峰2 孟庆昕3   

  1. 1. 华南理工大学 土木与交通学院,广东 广州 510640; 2. 中南大学 土木工程学院,湖南 长沙 410075;3. 中交宇科(北京)空间信息技术有限公司,北京 100101
  • 收稿日期:2014-04-08 修回日期:2014-10-23 出版日期:2015-04-25 发布日期:2015-03-03
  • 通信作者: 符锌砂(1955-),男,教授,博士生导师,主要从事道路规划与设计、智能交通系统等研究. E-mail:fuxinsha@163.com
  • 作者简介:符锌砂(1955-),男,教授,博士生导师,主要从事道路规划与设计、智能交通系统等研究.
  • 基金资助:

    国家自然科学基金资助项目(51178193);交通运输部西部课题(2011-318-365-100)

Detection Algorithm of Expressway Moving Objects Based on Background Subtraction

Fu Xin-sha1 Wang Xiang-bo1 Li Hai-feng2 Meng Qing-xin3   

  1. 1. School of Civil Engineering and Transportation,South China University of Technology,Guangzhou 510640Guangdong,China;2. School of Civil Engineering,Central South University,Changsha 410007,Hunan,China ;3. China Trans Geometrics Co. ,Ltd. ,Beijing 100101,China
  • Received:2014-04-08 Revised:2014-10-23 Online:2015-04-25 Published:2015-03-03
  • Contact: 符锌砂(1955-),男,教授,博士生导师,主要从事道路规划与设计、智能交通系统等研究. E-mail:fuxinsha@163.com
  • About author:符锌砂(1955-),男,教授,博士生导师,主要从事道路规划与设计、智能交通系统等研究.
  • Supported by:
    Supported by the National Natural Science Foundation of China(51178193)

摘要: 针对背景差分法难以适应光照变化频繁且对实时性要求较高的高速公路监控环境的问题,提出一种差分图像自适应阈值确定算法,利用统计学方法对差分图像中目标的灰度值进行快速有效的分类,并将分类界限作为自适应阈值,再利用差分图像的梯度分布辅助判断运动目标的区域. 试验结果表明,该算法可以适应不同的监控环境,能准确识别交通目标,且具有较好的稳定性.

关键词: 高速公路智能监控, 目标检测, 背景差分, 自适应阈值

Abstract: As the existing background subtraction algorithm is insufficient for the real-time expressway monitoring with frequent light change,an adaptive threshold determination algorithm based on the gray level is proposed. In this algorithm,the pixels of the object in the difference image are effectively classified by means of the statistical method,and the classification standard is defined as the adaptive threshold. Moreover,the gradient distribution of the difference image is used to judge the area of motion objects. Experimental results show that the proposed adap-tive threshold algorithm is effective in recognizing objects in different environments with high accuracy and stability.

Key words: expressway intelligent monitoring, object detection, background subtraction, adaptive threshold

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