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基于视频的交通数据采集关键技术及应用研究
其他题名Research on the Key Techniques and Applications of Video-Based Traffic Data Collection
王坤峰
学位类型工学博士
导师王飞跃 ; 汤淑明
2008-05-30
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
学位专业控制理论与控制工程
关键词智能交通系统 摄像机标定 运动目标检测与阴影去除 虚拟线圈 嵌入式交通数据采集系统 Its Camera Calibration Moving Object Detection And Shadow Removal Virtual Loop Embedded Traffic Data Collection System
摘要交通数据采集系统是智能交通系统的重要组成部分,在交通管理与控制、交通规划等方面发挥着重要作用。交通数据的采集离不开对道路上车辆的检测,与传统的地感线圈相比,视频检测有安装和维护方便、价格相对低廉、检测信息丰富、监视范围大等显著优点,逐渐成为近来研究的热点。然而,由于交通场景的复杂性和多样性,许多已有算法显得过于简单而不能满足实时性、准确性和鲁棒性要求,难以被实际应用。本文的主要工作,就是尝试为一些关键问题提供实时有效的解决办法。 基于视频的交通数据采集,涉及到许多共性问题,包括道路摄像机的标定、运动目标的检测、运动阴影的去除、交通参数的提取、嵌入式系统的开发等等。本文不但研究了相关基础理论,还面向实际应用,研制了嵌入式交通数据采集系统,并进行了长时间现场测试,获得了满意的效果。其主要贡献体现在以下几个方面: 1. 提出了一种操作简便的适合道路交通场合的摄像机标定方法。以求解摄像机参数方程组为核心,给出了在不同先验知识下的摄像机参数计算方法,使得算法非常实用。并通过实验证明,不同先验知识对应的摄像机标定策略虽然计算过程不同,本质上却是等效的,统一于摄像机参数方程组。 2. 探讨了三帧差分和背景消减相结合的运动目标检测方法,提高了复杂环境下运动目标检测的准确性。提出了基于8×8方格的连通区域提取方法,显著增强了运动目标的连通性,提高了对动态环境的自适应能力。 3. 提出了一种基于光谱的运动阴影去除方法。自动在线学习像素级阴影,计算阴影颜色模型;结合使用阴影的颜色和边缘模型,获得了较好的阴影检测效果;并通过空间分析去除半影,进一步降低阴影的错误否定和错误肯定。 4. 研究了基于虚拟线圈的交通数据采集方法。利用虚拟线圈内的运动特征和边缘特征,提出了一种新的车辆存在检测方法,该方法对光照剧烈变化、摄像机电路自动增益、运动阴影、夜晚车头灯光等常见噪声具有很强的免疫能力;通过与已有方法的实验对比,证明了所提算法具有计算量小、准确度高和鲁棒性强的特点。 5. 设计并实现了基于DSP的嵌入式交通数据采集系统。包括系统的整体结构设计和软件设计,将系统安装在路口进行长时间现场测试。获得了大量的路口交通数据,初步验证了系统长期工作时的准确性和鲁棒性。
其他摘要Traffic Data Collection System, as a part of Intelligent Transportation System (ITS), plays a significant role in traffic management and control, transportation planning and many other applications. Traffic data collection builds upon the detection of on-road vehicles; here, video detectors offer many advantages over traditional inductive loops. Although the field of video detection has been extensively explored in the past few years, due to the complexity and diversity of traffic scenes, many existing algorithms are too simple to satisfy the speed, accuracy and robust demands in practice. This thesis aims to develop efficient methods in solving some key problems. Video-based traffic data collection relates to many common research topics; including road camera calibration, moving object detection, moving shadow removal, traffic parameter extraction, embedded system development, and so on. In this thesis, we not only study some related fundamental theories, but also develop an embedded traffic data collection system for practical applications. The main contributions of this thesis are as follows: 1. We proposed a simple but convenient approach for camera calibration in traffic scenes. With the equation array of camera parameters as the kernel, various prior knowledge corresponds to various camera parameter recovery methods, making this approach very practical. Besides, experiments prove that different camera parameter recovery methods are essentially equivalent. 2. We discussed the moving object detection approach by combining three-frame difference and background subtraction. In particular, we propose an 8×8 pane-based connected component extraction method, which is very effective in improving the connectivity of moving objects and the adaptivity of algorithms in dynamic environments. 3. We proposed a spectrum-based moving shadow removal approach. First, through automatic and on-line learning of pixel-wise shadow, we calculate the shadow color model; then, combing the shadow color model and the shadow edge model, we obtained good shadow recognition effects; finally, we perform spatial analysis in order to eliminate the penumbra and further reduce the false positives and the false negatives of the shadow. 4. We studied the virtual loop-based traffic data collection approach. Using the pixel motion and edge features in the virtual loop, we propose a novel vehicle detection method, which is not sensitive to a wide variety of artifacts. Experimental comparisons with a state-of-the-art method prove that the proposed method has low computation load, high accuracy, and a strong robustness. 5. We designed and implemented a DSP-based embedded traffic data collection system. We obtained a large amount of intersection traffic data, which initially confirmed the accuracy and robustness of the system working in traffic scenes over a long time span.
馆藏号XWLW1258
其他标识符200518014628018
语种中文
文献类型学位论文
条目标识符http://ir.ia.ac.cn/handle/173211/6099
专题毕业生_博士学位论文
推荐引用方式
GB/T 7714
王坤峰. 基于视频的交通数据采集关键技术及应用研究[D]. 中国科学院自动化研究所. 中国科学院研究生院,2008.
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