【作者】 宋慧敏；

【导师】 徐华中；

【作者基本信息】 武汉理工大学 ， 控制理论与控制工程， 2008， 硕士

【摘要】 智能交通系统(ITS)是未来交通系统的发展方向,它是将先进的信息技术、数据通讯传输技术、电子传感技术、控制技术及计算机技术等有效地集成运用于整个地面交通管理系统而建立的一种在大范围内、全方位发挥作用的、实时、准确、高效的综合交通运输管理系统。先进的交通检测技术和计算处理技术是智能交通系统中的一个重要研究领域。本文以交通路口单个固定摄像头拍摄的视频图像为研究对象,提出了一种基于车道检测的车辆检测方法,将通常运用于车载系统的车道线检测技术运用于环境较复杂的交通路口车辆检测系统,为车辆检测自动划分感兴趣区域,大大降低了车辆检测的计算量。论文首先总结了常用的目标检测方法及背景建模方法,并对它们进行分析和对比,从而选择背景帧差法进行车辆检测,选择时间中值法进行背景建模,并且提出了自己的背景更新策略。随后,本文根据所得到的背景图像的特征,对图像进行滤波处理。在对常用的图像预处理算法进行总结和对比后,选择了适合道路背景图像的边缘检测算法,提出了一种基于灰度值选择的道路背景滤波方法,并且对传统的Hough变换方法进行了改进。然后,本文根据得到的车道信息,划分了车辆检测的感兴趣区域,使用阈值分割法将车辆目标分割出来,采用连通域标识法得到目标的最小外接矩形,通过计算外接矩形的个数来完成车辆计数。最后,本文使用固定相机拍摄的一段2分47秒的路面视频来对算法进行检验。实验表明,本文提出的算法可以有效地检测出晴好天气下结构化道路上的车道线,对车辆的检测有较高的正确率。尽管如此,本文还有许多有待改进的地方,如还需要进一步的研究,使得本系统能检测更多的交通车辆参数,比如对车辆速度、长度等的检测;也需要进一步的改进,使得该系统能运用于环境更为复杂的非结构化道路中。更多还原

【Abstract】 Intelligent Transportation System (ITS) is the development trend of transportation system. Advanced information technology, data communication Technology, electronic sensing technology, control technology and computer technology are integrated in ITS, which constitute the comprehensive, effective, accurate and real-time transportation system. Advanced traffic monitoring and computer processing technology is an important area in ITS.The videos captured by a static camera are studied in this paper, and a vehicle detection method based on road detection is proposed. The road detection which is typically used in in-car system, is used to determine the interest areas of vehicle detection. It efficiently reduces the complexity of the vehicle detection.The paper first gives a summary of motion detection methods and background modeling methods, analyzes and compares them with each other, and chooses the background subtraction method to detect the vehicles, uses Time Median Background Model Method to form a background image, then gives a background update method.In the second part, it uses filters to reduce the noises, according to the features of the background image. The summary and comparison of image pre-processing algorithm is discussed in this paper, and the appropriate edge detection method is chosen, a road detection algorithm based on gray value chosen is raised, the traditional Hough Transform is improved.According to the information of roads, it determines the interest areas of vehicle detection, uses a threshold operation to get the moving object, and apply Clustering algorithm to form the MERs(Minimum Enclosing Rectangle), and then takes count of MERs to finish the vehicle counted.At the end, a road video which continues 2minute and 47second , is used to test the system improved by this paper. Test Results show our system can efficiently detect the road-line in clear day, and get a high rate of recognition. However, it still requires more work on how to compute traffic parameters in the future, like speed, vehicle length, etc. It also need more research on how to apply this system to the unstructured road which have a more flexible environment.更多还原