【作者】 杜晶晶；

【导师】 金炜东；

【作者基本信息】 西南交通大学 ， 信号与信息处理， 2009， 硕士

【摘要】 智能视频监控是模式识别和计算机视觉领域的主要内容之一,在军事、医学和科研等领域得到了广泛的应用。智能视频监控系统中运动目标检测与跟踪算法的设计是系统的核心,因此,研究智能视频监控系统中的关键技术并提高智能视频监控系统的性能具有重要的意义。本文针对目标检测与跟踪算法中所存在的主要问题进行了研究,并对相应的问题给出了解决方案。本文的主要研究工作如下:在目标检测方面,首先对三种常用方法:光流法、背景差分法、帧间差分法进行了分析讨论,并重点研究了三帧差分算法。通过实验表明标准三帧差分算法检测出的运动目标区域不够完整,并且在目标重叠部分不容易检测出来。为了完整、准确地检测出运动目标,本文在原有三帧差分原理基础上进行改进,给出了基于五帧差分的目标检测方法。并通过实验比较了标准三帧差分、增加图像预处理的三帧差分以及五帧差分的检测效果。实验表明,五帧差分法能有效地消除了拖影和空洞现象,更能完整地提取目标,为后面目标特征提取工作打好了基础。在目标跟踪方面,利用投影法和一阶矩精确地确定了目标的质心位置。给出综合使用目标质心、面积以及矩特征作为目标特征,有效地对目标进行了描述,并给出了基于欧式距离的目标匹配算法。本文利用Kalman滤波器建立运动模型,预测被跟踪目标在下一帧中可能出现的位置,并确定搜索范围,再结合本文给出的目标匹配算法,有效地对运动目标进行了跟踪。最后对多目标中的遮挡问题进行了研究,给出一种在遮挡情况下的目标跟踪方法。对跟踪算法进行了实验仿真,给出了实验数据并对其进行了分析。实验结果表明,基于卡尔曼滤波的目标跟踪模型比较符合实际的情况,跟踪算法能可靠地预测与跟踪目标的运动轨迹。最后,结合本文改进的运动目标检测方法和基于Kalman滤波器的跟踪模型,采用VC++开发平台和OpenCV算法库设计了一个智能监控的演示系统。通过该演示系统初步验证了本文算法的可行性和可实现性。更多还原

【Abstract】 Intelligent video surveillance is one of the main content in the field of pattern recognition and computer vision, which has been widely applied in military, medicine and scientific research etc. The design of Moving target detection and tracking algorithm become a core in the intelligent video surveillance system. Therefore, the study of the key technology in intelligent video surveillance system and to improve the performance of the system is of great significance.The thesis studied the main problem of the target detection and tracking algorithm, and gives the solution to the corresponding problem. The main research work in this article as follows:On the research of the motion detection, firstly three main algorithms of motion detection which is Optical flow method, the background difference method, inter-frame difference method are researched, the thesis focus on the three differential algorithm. The experimental results show that the region of moving targets aren’t integrity, there is a certain shadow, inanition and so on. In order to completely and accurately detect moving targets, the thesis gives a new target detection method based on the five differential, this target detection method is the improvement based on original three differential. The experiment compared the detection effect of the standards three differential, increasing image pre-processing on three differential and five differential detection. The experimental results show the five differential can effectively eliminate the phenomenon of shadow and inanition, more completely extract the target, and make a good foundation for the back of Target Feature Extraction.On the research of object tracking, the use of projection and the first moment to pinpoint the target’s centroid. The thesis integrated use of the target centroid, and moment characteristics of the area as the target characteristics, effectively described the target, and gives the matching algorithm based on the European-style distance. The thesis establish the motion model using Kalman filter, forecast the Possible location in the next frame of the tracked goal, and determine the scope of the search, and then combined with the goal matching algorithm of this paper, effectively track moving targets. Finally, research on the occlusion of more targets, and gives target tracking method in case of shelter. The thesis did the experiment simulation to tracking algorithm, and gave the experimental data and analyzed. The experimental results show that target tracking model accord with actual situation based on Kalman filter, the tracking algorithm can reliably predict the movement and track the target trajectory.Finally, combined with the improved methods of moving target detection and tracking model based on Kalman filter, using VC + + development platform and OpenCV algorithms library designed a demonstration of intelligent monitoring system. Through the demonstration of the system initially verify the feasibility and achievability of the algorithm.更多还原