【作者】 张明；

【导师】 冯远静；

【作者基本信息】 浙江工业大学 ， 控制理论与控制工程， 2014， 硕士

【摘要】 经济的发展加快了城市化的进程,城市中汽车保有量急剧增加,由此引起的交通问题已经成为各大城市共同面临的难题。其中,道路交叉口是城市路网的节点,是车流的主要集散场所,也是交通拥堵发生的重灾区。因此,合理地设置交叉口的信号配时方案对交叉口的通行效率有着极其重要的意义。实时、充分、准确地获取路网中的交通运行状态,才能为交通信号配时方案的选择与优化提供可靠的数据。而实际路网中能提供实时交通信息的检测器相对较少,且容易损坏,无法为交通信号控制系统提供所需的数据。与此同时,道路视频监控系统的视频采集设备已广泛分布于城市路网中,如能有效获取采集的视频数据,并检测其中蕴含的交通信息,则可以为交通信号控制提供有价值的参数信息。本文将道路监控视频作为交通参数获取的数据源,提出了基于视频图像处理的交通参数检测系统框架,并用检测到的交通参数有效优化交叉口的信号配时方案。本文的主要内容与创新点如下：(1)针对现有道路监控系统中视频采集设备与视频数据封装格式的多样性,对不同设备提供商的设备开发相对应的视频解封装程序。实现在不改造现有道路视频监控平台硬件的情况下,无缝接入平台,将视频监控数据纳入实时交通参数检测的数据源,极大的扩展了交通参数的来源,丰富了交通参数采集手段。(2)针对干线道路连续交通流特性,提出了一种快速车辆检测算法,该算法使用第一帧视频图像快速建立背景模型,从第二帧开始检测运动车辆,并将检测得到的不同区域,设定不同的更新概率来更新该点及其邻域的背景样本,实现背景模型的快速动态更新。再使用基于虚拟线圈的车流量检测算法,准确统计得到路段车流量。(3)针对交叉口间断交通流特性,采用一种选择背景建模算法,该算法首先利用边缘信息和纹理特征筛选出一些无车存在的图片,以此来建立背景模型,再利用背景差法与颜色空间计算模型,充分利用交通场景中的颜色信息,精确检测车辆。并提出了一种基于动态阈值分割的车流量统计算法,统计得到准确的路口车流量和分向车流量。(4)根据现有道路视频监控系统中视频检测器的空间分布,以及检测所得交通参数的时空相关性,提出了一种新的交通信号控制算法。通过上游的视频检测器检测得到交通参数,使下游交叉口的信号控制器根据当前的交通状况并结合即将到来的交通流生成最优的信号配时方案,通过VISSIM实验仿真,该算法可有效提升道路的通行能力,缩短车辆延误时间,具有较好的控制效果。更多还原

【Abstract】 The development of economy speeds up the progress of urbanism. As the rapidly increase of vehicles in urban, which caused a difficult traffic problem to all of the big city. Intersection is the city traffic node, the main location of flowing and the key section for congestion of the city. Therefore, the reasonable signal control plan of intersection has an important significance for traffic control. In order to provide reliable data for the selection and optimization of traffic signal control, we must identify the traffic condition timely and accurately. In reality, the number of installed traffic detectors is much less than the requirement of traffic signal control. On the other hand, there exists a considerable amount of video monitoring cameras in the network for purpose of traffic surveillance and monitoring. These cameras could provide a sequence of images of real-time traffic condition. If we can use these videos to estimate the real-time traffic condition, it is very meaningful for traffic signal control.In view of this, a new image processing method is proposed to estimate traffic flow parameters by using these videos and we use the detected parameters to optimize the traffic signal control plans of the intersection. The main contributions of this study are summarized as follows:(1) The cameras in road monitoring system are provided by different device providers, which makes these videos very difficult to be processed by using existing video-based methods, therefore we develop appropriate programs to obtain the available image data. The proposed method has practical advances as it could achieve accurate available traffic image data without any requirement of modification to existing video monitoring system.(2) In order to detect moving vehicle quickly, we present a samples-based adaptive segmentation detection algorithm for highway scene. Firstly, selecting the first frame as a referenced frame, and using a random policy to select values to build a samples-based estimation of the background. Secondly, adopting different strategies to update different regions, and using frames subtraction and background extend detection to classify the pixels in each frame into background area, uncovered background area and background extend area. Finally, using different rate to update these areas and virtual coil based detection algorithm to count the traffic flow.(3) We present a new traffic parameters extraction approach for traffic surveillance system at an urban intersection. Firstly, as the low occurrence probability of background pixels, presenting a selective background model (SBM) framework, then using the edge information and texture feature to select the appropriate candidate pictures and generate the background image. Secondly, segmenting moving objects by using background subtraction, then combining them with edge information and texture feature to determine whether there is a vehicle in the detection zone. On this basis, we use dynamic threshold to optimize the vehicle count result.(4) According to the spatial distribution of the video detector in existing road monitoring system, we present a new intelligent signal control algorithm to detect spatial and temporal correlation parameters. The upstream video detector detects the traffic parameters, and then notifies downstream signal controller which use the current traffic condition and the upcoming traffic flow to optimize traffic signal control plans.更多还原