基于视频检测的行人交通参数提取研究硕士学位论文.doc
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1、硕士学位论文基于视频检测的行人交通参数提取技术研究Extraction Technology of Pedestrian Traffic Parameters Based on Video Detection 学位论文版权使用授权书本学位论文作者完全了解北京交通大学有关保留、使用学位论文的规定。特授权北京交通大学可以将学位论文的全部或部分内容编入有关数据库进行检索,提供阅览服务,并采用影印、缩印或扫描等复制手段保存、汇编以供查阅和借阅。同意学校向国家有关部门或机构送交论文的复印件和磁盘。(保密的学位论文在解密后适用本授权说明)学位论文作者签名: 导师签名:签字日期:年月日签字日期:年月日中图分
2、类号: 学校代码:10004UDC:密级:公开北京交通大学学位论文基于视频检测的行人交通参数提取技术研究Extraction Technology of Pedestrian Traffic Parameters Based on Video Detection作者姓名: 学 号: 导师姓名: 职 称: 学位类别:工学 学位级别:硕士学科专业:交通运输规划与管理 研究方向:城市交通规划与管理北京交通大学2019年10月63北京交通大学硕士学位论文ABSTRACT中文摘要摘要:目前交通监控的主要方式是两种:人工观察;摄像机记录,人工统计。这两种方式都需要投入大量的人力和物力资源,并且人工统计的准
3、确性有限,可能会出现疏漏,对异常情况不能及时做出反应。为此需要建立一种不需人工干预、或者只需要很少人工操作的智能交通管理系统,通过安装在固定位置的摄像机拍摄视频,实现对目标的定位、识别和跟踪及交通参数的提取和分析,并在此基础上进一步实现目标(例如行人、车辆等)行为的分析与判断。目前,针对车辆的目标检测、跟踪、识别研究已经比较成熟,但针对行人的目标检测、跟踪、识别研究则相对滞后,而且具体情况异常复杂,例如行人为非刚体,在视频中的面积小,行动较为随机,速度并不连贯,而且行人之间的间距有可能较小,这些都给相关技术造成了很大的挑战。本文对上述难点进行了研究,通过提取交叉口的行人流量、速度、步行方向等参
4、数,给行人的交通管理与控制以及仿真系统模拟现实提供基础数据。本文的主要研究内容包括:(1)分析了智能交通监控视频中的降质因素,并研究了各种噪声的数学模型,针对交通视频中最常见的椒盐噪声和高斯噪声实现了经典滤波算法的去噪,并设计了一种改进算法,将加入图像像素点是否为噪声的判决预处理,只针对可能为噪声点的区域进行去噪,而对于非噪声点不进行去噪,更好地保持了图像的细节信息。(2)对于行人检测和识别问题,针对各种具体难点设计了具体的解决方案和步骤。比较了常用目标检测算法,并根据交叉口智能交通监控视频的特点选用了合适的行人检测算法。由于检测算法可能造成区域的不完整和空洞,采用形态学操作对行人目标区域的断
5、裂区域实现连接,并对目标区域内的空洞进行填充。对可能存在噪声的区域采用区域面积统计的方法计算面积,并去除小面积的噪声斑块。对阴影采用几何形状的方法予以去除。对交叉路口复杂环境中的各种运动目标进行建模,并总结了行人目标的特点,实现了基于视频检测的行人目标识别。(3)实现了交通视频中交叉口行人目标的跟踪。通过对摄像机视场的标定,完成了摄像机空间拍摄的视频数字图像中的坐标到实际物理世界空间坐标系的映射换算,提取了包括行人目标的质心、位移、速度、加速度和流量等交通参数,为仿真系统模拟现实提供实际状况提供了翔实的基础数据。综上所述,本文的研究成果能够为仿真系统模拟现实提供实际状况,并分析所在路口的交通状
6、况,为交通管理与控制提供依据。关键词:交通参数提取;行人检测;行人识别;行人跟踪;视场标定ABSTRACTABSTRACT: Currently, most monitoring of traffic scene is mainly achieved through manual supervisory control or artificial observation of video after the event. These methods need a lot of manpower, material resources; furthermore, omissions may oc
7、cur abnormal situations cant be responded due to the limitation of humans energy and attention. Thus there is need to establish intelligent traffic management system that is without human intervention or requires very little manual operation. In the system, installing cameras are fixed to capture vi
8、deo to achieve targets location, identification and tracking. Behavior of goals (such as pedestrians, vehicles, etc.) are analyzed and judged based on the traffic parameters that are analyzed and extracted.The methods of vehicle detection, tracking and recognition have been proposed and achieved som
9、e fruits and contributions, however, pedestrian detection, tracking and recognition is relatively lagging behind. Besides, the specific situation of pedestrian is much more complex. The pedestrian area in the video is small; the action of pedestrian is random; the speed of pedestrian is not consiste
10、nt; and the spacing between the pedestrians may be very small. The above problems pose a great threat on technological realization. Therefore, it is required to search and solve the difficulties of parameters extracting, including the amount of pedestrian on intersection of traffic, speed, direction
11、 and other parameters important traffic data which are important to the simulation system that can simulate the actual state of reality. The main contents of this paper include:(1) The lower quality factors of the intelligent traffic surveillance video were analyzed and various noise models are summ
12、arizes. The most common noise in intelligent traffic video, salt &pepper noise and Gaussian noise, were reduced by classical filtering algorithms. An improved algorithm was proposed in which the image pixels will be pre-judged as noise or non-noise. Only possible pixels in the region of noise were f
13、iltered while non-noise pixels were not filtered, thus the image details were remained better.(2) For pedestrian detection and identification, specific solutions and specific steps were developed to solve the difficulties. The commonly algorithms used for target detection were compared and suitable
14、pedestrian detection algorithm was selected according to the characteristics intelligent traffic intersection surveillance video. The detected region of pedestrians is often incomplete and has empty regions, morphological operations were employed on the pedestrian region to connect the fracture of t
15、he target area and fill the holes in the target region. Small noise patches that may exist in region of pedestrians were removed by area calculation. The shadow of pedestrian was removed by geometric-based method. Various moving object models in the complex environment of intersection were establish
16、ed and the characteristics of pedestrian goals were summarized to achieve video-based pedestrian detection and recognition.(3) Pedestrian tracking algorithm in intelligent traffic video intersections was realized. Through calibration of the camera field, the camera space of digital image coordinates
17、 in video was mapped to the actual physical world space coordinate. A lot of important traffic parameters were extracted, including center of pedestrian target, displacement, walking speed, walking acceleration, flow rate of pedestrian traffic and so on. The valuable traffic information and data are
18、 important for the simulation system to provide realistic simulation of the actual situation.In conclusion, the results and conclusions of this research project can provide practical simulation system with the real traffic situation which analyzes the traffic situation in the junction of the transpo
19、rtation system. Besides, it provides the basis for comprehensive management and maintenance. Thus, this paper is not only of great theoretical significance, but also has extensive application value.KEYWORDS:Traffic parameter extraction; pedestrian detection; pedestrian recognition; pedestrian tracki
20、ng; field calibration北京交通大学学位论文目录目录中文摘要vABSTRACTvi1 绪论101.1 研究背景和意义101.2 智能交通系统概况111.3 国内外研究现状121.4 主要研究内容和论文框架132 交通图像预处理152.1 视频图像降质模型152.2 常用的滤波去噪模型162.2.1 空间域滤波器162.2.2 频率域滤波器172.3 去噪实验及分析182.3.1 椒盐噪声182.3.2 高斯噪声222.4 本章小结263 基于视频检测的行人检测与识别273.1 常用检测算法273.1.1 背景消除法273.1.2 光流场法283.1.3 帧间差分法293.2
21、本文行人检测方法303.3 区域处理323.3.1 形态学概述323.3.2 连接断裂区域333.3.3 空洞填充343.4 噪声区域抑制353.4.1 噪声斑块去除353.4.2 阴影去除383.5 基于视频检测的行人识别算法413.5.1 运动目标特征建模413.5.2 行人运动目标判断准则423.6 本章小结444 行人跟踪与交通参数提取464.1 常用跟踪算法464.1.1 基于特征的跟踪464.1.2 基于3D模型的跟踪464.1.3 基于活动轮廓的跟踪474.1.4 基于区域的跟踪474.2 本文交叉口行人的跟踪484.2.1 Mean Shift概述484.2.2 基于Mean
22、Shift的行人跟踪494.3 摄像机视场的标定和转换504.3.1 标定原理504.3.2 本文的标定方法514.3.3 标定过程与结果524.4 交通参数提取554.4.1 质心554.4.2 位移564.4.3 步行速度564.4.4 加速度574.4.5 流量584.5 本章小结605 总结与展望61参考文献63作者简历66独创性声明67学位论文数据集68北京交通大学硕士学位论文总结与展望1 绪论1.1 研究背景和意义我国的交通事业经过了几十年的快速发展,已取得了突飞猛进的进步。但随着快速发展给人们生活带来极大便利的同时,也引起了各种安全隐患。例如交通事故发生率逐年上升,交通拥挤现象也
23、成为大城市亟待解决的交通问题。交叉路口是交通事故和交通堵塞的频发地带,因此需要对交叉路口实现交通视频的监控,避免和减少事故和堵塞的发生。目前对高速路出入口、事故多发路口、停车场、住宅小区出入口等交通场景监控的主要方式是通过安排专人监看。不仅需要耗费大量的人力和物力资源,并且由于人工的方式准确度是有限的,可能会出现在高强度工作环境下发生错误,从而不能对特殊情况及时做出反应。为了解决这些问题,在一些交通场景中也使用了摄像机进行录像,事后再采用人工观察的手段来进行统计,但是这种做法仍然需要大量的人工辅助工作,同样无法对特殊情况做出实时的应对。目前,监测的手段大部分是使用环形线圈传感器来对交通的情况进
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