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智能交通中基于视频的交通流量检测算法研究
其他题名In the Intelligent Traffic Research of Video-based Traffic Flow Parameter Measurement
杨永辉
学位类型工学硕士
导师刘昌平
2011-05-17
学位授予单位中国科学院研究生院
学位授予地点中国科学院自动化研究所
学位专业计算机应用技术
关键词视频监控 智能交通 车辆计数 车辆排队检测 摄像机标定 Visual Surveillance Intelligent Traffic Vehicle Counting Vehicle Queue Camera Calibration
摘要智能交通是解决当今由于经济发展所带来的交通问题的根本办法。交通信息的获取是智能交通中的一个基本问题。传统上,这些数据是通过地感线圈给出的,但是由于其测量范围的限制,已经越来越不能满足理论研究和实际应用的需求。本文采用了基于视频分析的交通流量检测方法。视频的优点是能实时的提供大范围交通场景的数据。基于视频分析的交通信息采集系统可以获得的信息很多,如:车道流量、速度、占有率、车辆类型、车牌号码、交通事故等。本文主要对智能交通系统中交通流量统计的主要方法进行了研究。主要内容包括:基本的车辆检测算法、车辆计数算法、适合交通监控场景的摄像机标定算法和车辆排队长度检测算法等。具体工作主要包括以下两个方面: 1. 针对现有车辆计数算法的不足,进行了改进,实现了一个基于虚拟线圈的车辆计数系统,该系统在多种光照和交通状况下都可准确的进行车辆计数。该系统在白天和夜晚采用了不同车辆计数算法:白天情况改进了现有计数方法在车辆相连时漏检和车辆因拥堵而时动时停时造成重复计数的缺点;在夜晚复杂的光照条件下,利用车灯这一夜晚车辆的显著特征进行车辆检测,实现车辆准确计数。同时提出一种白天夜晚判别的算法实现计数算法的自动切换。 2. 针对现有车辆排队长度检测系统不能在夜晚很好地工作和无法给出队列实际长度的不足,本文实现了一个完整的车辆排队长度检测算法。对白天和夜晚不同光照条件采用不同的队列检测算法:在白天光照条件下,采用移动检测窗来进行车辆排队检测,在每一个检测窗内,通过三帧差法运动检测和形态学边缘检测进行车辆存在检测两步判断有无车辆排队;针对夜晚场景,同样采用移动检测窗,然后通过车灯这一显著特征进行车辆检测实现车辆排队检测。队列长度计算通过摄像机标定完成,找到一种仅利用车道线的、简单有效的摄像机标定方法。实验表明该方法可以准确检测出车辆排队并计算出其度。
其他摘要The Intelligent Traffic System (ITS) is the fundamental way to solve the traffic problem produced along with the economic development.The collection of traffic information is a basic problem in Intelligent Traffic. Traditionally,the data are usually measured by buried magnetic loops. But the loops can not meet with the requirement of the theory and practical application because of their limited scope. So the video-based method is presented in the thesis to solve the problem, which can supply with real-time data in the large-scale traffic scene. Video-based traffic information collection system can get a lot of information available, such as: traffic load,travel speed, lane occupancy, vehicle type, license plate number, traffic accidents, etc.. This paper focuses on the research of the collection of traffic flow. The main contents include: vehicle detection algorithm, vehicle counting algorithm, the camera calibration algorithm and vehicle queue length detection algorithm. With the research subject, the main contributions of the thesis include the following aspects: 1. In consideration of the lack of existing vehicle counting algorithms, we achieved a virtual loops based vehicle counting system。In variety of lighting and traffic conditions the vehicle counting system can count vehicles accurately. We exploited different counting methods at daytime and nighttime. In the the daytime we improved the existing algorithms to solve the missing counting for connected vehicles and repeated counting for congestion situation.In the night,the lighting condition is more complex , we achieved vehicle counting through detecting vehicle’s headlights.And a kNN classification was used to switch detection methods between daytime and nighttime. 2. For the lack of the existing vehicle queue length detection system, in this paper a complete vehicle queue length measurement system was achieved, consisted of vehicle queue detection and queue length measurement. Weexploited different detection methods at daytime and nighttime. At daytime, the queue detection algorithm consists of vehicle motion detection and vehicle presence detection, and a profile consisting of small profiles with variable sizes was used. In the night, a novel queue detection method through detecting vehicle’s headlights was achieved. And found a simple camera calibration method just using the geometry properties of road lane marking, then we could easily calculate the queue length. Experiment results indi...
馆藏号XWLW1653
其他标识符200828014629086
语种中文
文献类型学位论文
条目标识符http://ir.ia.ac.cn/handle/173211/7584
专题毕业生_硕士学位论文
推荐引用方式
GB/T 7714
杨永辉. 智能交通中基于视频的交通流量检测算法研究[D]. 中国科学院自动化研究所. 中国科学院研究生院,2011.
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