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一种基于运动目标识别的智能视频监控系统 被引量:8

An Intelligent Video Surveillance System Based on Moving Target Recognition
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摘要 针对武器试验现场飞行目标降落中事故多发的现状,设计一种基于运动目标识别技术的智能视频监控系统。系统采用采集前端-服务器端-决策模型。利用背景差分法和帧间差分法检测运动目标图像,从而实现视频运动目标的自动分割;采用轮廓提取和边缘检测技术进行目标识别;最后用特征算法提取目标的特征参数,从而判断目标的参数信息是否会对降落安全构成威胁,做出进一步决策。对实现系统功能所需的关键技术进行详细介绍,在Visual C++6.0中用OpenCV实现相关算法的设计,并给出部分关键代码。仿真实验结果表明:该系统能满足智能监控的基本要求,精确显示运动目标在靶面上的位置,可根据客户需求识别运动目标。 Aiming to the actuality of more accidents happened on flying target landing in the weapon testing field,an intelligent video surveillance system based on moving target recognition was designed.The system adopts capturing front-server-decision model.At first,a method for detecting moving targets images using background difference method and frame difference method is introduced.Secondly,target recognition is studied by the technology of contour extraction and edge detection.Finally,characteristic parameters of target are extracted by feature algorithm.Based on it,key technologies involved in the system are described in detail.Additionally,corresponding algorithm is designed using OpenCV in Visual C++ 6.0,and part of the key codes are given.Simulation results shows that the system designed can meet the needs of monitor and control of flying target,and also verify the effectiveness of the algorithm.
出处 《兵工自动化》 2012年第3期5-9,共5页 Ordnance Industry Automation
基金 陕西省科学技术研究发展计划项目(2011K06-22)
关键词 智能视频监控系统 目标识别 检测运动目标 特征参数 OPENCV intelligent video surveillance system target recognition detecting moving targets characteristic parameters OpenCV
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  • 1李防震,胡匡祜.非刚性运动分析方法的现状与展望[J].中国图象图形学报(A辑),2005,10(1):11-17. 被引量:3
  • 2刘远志,潘宏侠,赵润鹏.基于OpenCV和Adaboost算法的人脸检测[J].机械管理开发,2012,27(1):185-186. 被引量:6
  • 3胡以静,李政访,胡跃明.基于光流的运动分析理论及应用[J].计算机测量与控制,2007,15(2):219-221. 被引量:29
  • 4陈伯时.电力拖动自动控制系统[M].3版.北京:机械工业出版社,2003.
  • 5徐霞平.复杂交通环境下的人体运动目标识别算法研究[J].长沙理工大学,2012.
  • 6Yoav Freundand Robert E.Schapire. Adecision the oreticgeneralization of online learning and an application to boosting. Journal of Computer and System Sciences,55(1):119 139,August 1997.
  • 7Paul Viola,Michael Jones. Rapid Object Detection Using adaboosted Cascade of Simple Feature[C]. Computer Vision and Pattern Recognition, 2001.
  • 8Paul Viola,Michael Jones. Robust Real Time Object Detection[C]. ln:Proc. Of IEEE Workshop and Statistical and Computational Theories of Vision, 2001.
  • 9Rainer Lienhart and Jochen Maydt. An Extened Set of Haar-like Features For Rapid Object Dectection[M]. Submitted to ICIP2002.
  • 10Alexander Kuranov, Rainer Lienhart,and Vadim Pisarevsky. An Empirical Analysis of Boosting Algorithms for Rapid With An Extended Set of Haar-like Features[C]. lntel Technical Report MRL- TRJuly02-01,2002.

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