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基于三维模型的路面车辆定位与跟踪
其他题名3D MODEL BASED VEHICLE LOCALIZATION AND TRACKING
仰颢
学位类型工学硕士
导师谭铁牛
2001-05-01
学位授予单位中国科学院自动化研究所
学位授予地点中国科学院自动化研究所
学位专业模式识别与智能系统
关键词交通监控 车辆定位 车辆跟踪 遮挡处理 Traffic Surveillance Vehicle Localization Vehicle Tracking Occlusion Rasoning
摘要视觉交通监控技术是在不需要人的干预、或者只需要很少干预的情况下,通过对 摄像机拍录的视频序列进行分析来实现车辆的定位、识别和跟踪,并在此基础上分析 和判断车辆的行为。在路面交通管理和高速公路出入口、收费站、事故多发地段、停 车场、高级住宅小区等交通场景的监控等方面,视觉交通监控技术都有着广泛的应用 前景。在过去的十年中,它一直是计算机视觉领域中的一个热门问题,并且受到了越 来越多的学者的密切注意。但是,到目前为止,视觉交通监控技术还没有达到大规模 实际应用的成熟程度,它的局限性主要体现在三个方面:精度、速度和稳定性。 有鉴于此,本文围绕视觉交通监控技术中的一个基本问题,也就是路面车辆的定 位与跟踪,展开了比较深入的研究,提出了一些新的算法,并在此基础上设计和参与 实现了一个车辆跟踪原型系统: 1)提出了一种基于三维线框模型的车辆定位算法。它将定位过程假想成从初始 姿态到正确姿态的一系列虚拟运动,并进一步分解为两种独立的运动:平移 和旋转,然后分别闭式地确定这两种运动的参数。实验结果表明,该算法可 以快速、准确、鲁棒地根据一幅灰度图像确定其中车辆在三维空间里的姿态。 2)提出了一种基于改进的扩展卡尔曼滤波器的车辆跟踪算法。其中包括一种新 的车辆运动学模型,考虑了车辆行驶过程中的一些物理性质,而不是将整个 车辆视为一个质点。该算法还利用了一种改进的扩展卡尔曼滤波器,通过强 制残差序列满足正交性条件来保证残差序列拥有与白噪声相同的性质,从而 满足了卡尔曼滤波器中对于观测噪声是白噪声的假定。实验结果表明,当车 辆的运动急剧、复杂时,该算法比经典的扩展卡尔曼滤波器要稍好一些。 3)提出了一种基于部分匹配的遮挡处理算法。该算法在目标被遮挡时自动调整 匹配模板,从模型投影中选耳义一个区域来和图像进行匹配。该算法还将遮挡 区分为三种情况:目标与背景之间的遮挡、目标离开视野时的遮挡、以及多 目标之间的遮挡,并对它们分别提出了处理策略。实验结果表明,即使在车 辆发生严重遮挡的时候,陔算法仍然能够获得较好的结果。 4)设计并参与实现了一个交通监控原型系统。交通监控是一个系统层次上的课 题,它不仅具有明确的应用背景,而且涉及到计算机视觉和图像处理中的很 多基本问题,因此一个原型系统对于交通监控领域内的研究是至关重要的。 在借
其他摘要Visual traffic surveillance is to perform recognition, localization and tracking of road vehicles based on video sequences captured by cameras without human intervention or with little human intervention, and further analysis vehicles' behaviours according to the tracking results. It has important potential applications in the area of traffic management and surveillance for particular scenarios, such as highway entrance and exit, toll station, dangerous section, parking lot, residential quarter, etc. In the past decade, visual traffic surveillance has been a popular issue in computer vision with increasing concern. However, it has not reached the maturity for large-scale applications yet, while the primary limitation lies in three aspects: precision, processing speed, robustness. In this thesis, we present a number of novel algorithms for road vehicle localization and tracking from monocular image sequences, which is a fundamental problem in visual traffic surveillance. Based on these algorithms, we have designed and implemented a prototype vehicle tracking system, which is also introduced in the end of the thesis. The contributions of this thesis include: 1) A novel 3D wireframe model based vehicle localization algorithm is presented. It considers localization process as a series of virtual motions, which can be decomposed into two kinds of independent motions: translation and rotation. The motion parameters for both of them can be determined in closed form. Experiment results show that it can efficiently and robustly derive vehicles' 3D pose from one intensity image with desirable precision. 2) An improved Extended Kalman Filter (EKF) based vehicle tracking algorithm is presented. Considering some physical characters of moving vehicles, we introduce a new dynamic model for their motion. The novelty of our dynamic model is that the internal structures of vehicles are taken into account, while most existing algorithms consider moving vehicles as particles. Our algorithm also utilize an improved EKF, which incorporates the orthogonality condition into traditional EKF to counteract the instability of model parameters, thus satisfying the EKF's assumption of measurement noise's being White Noise. Experiment results show that it can obtain better performance than traditional EKF under complicated driving maneuver. 3) A novel partial match based occlusion reasoning approach is presented. It can automatically adjust the model to be matched with the image when occlusion occurs. We divide occlusions into three kinds, which include vehicles occluded by the background, vehicles occluded when entering or leaving the view of camera, vehicles occluded by other vehicles, and present processing strategies for all of them. Experiment results show that it can still obtain goo
馆藏号XWLW593
其他标识符593
语种中文
文献类型学位论文
条目标识符http://ir.ia.ac.cn/handle/173211/7328
专题毕业生_硕士学位论文
推荐引用方式
GB/T 7714
仰颢. 基于三维模型的路面车辆定位与跟踪[D]. 中国科学院自动化研究所. 中国科学院自动化研究所,2001.
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