基于图像处理的车辆外形测量技术研究

发布时间：2018-06-27 06:13

本文选题：车型识别 + 车辆测量　；参考：《长安大学》2017年硕士论文

【摘要】：车型的自动识别技术是ITS系统的关键技术之一,而车辆外形的三维尺寸能够为车型识别提供可靠有效的数据基础。传统的车辆外形测量方法主要依赖于激光雷达,它的测量精度较高,但容易受天气和大气的影响,而且成本较高,不利于大规模的实际应用。本文结合交通应用场景,研究了一种基于图像处理的车辆外形测量方法,通过图像处理技术,实现车辆外形的长宽高测量。其中,针对不同的应用场景,本文提出了基于平面图像和基于深度图像的两种实现方案,它们都采用相机近距离安装的方式获取清晰的车辆图像,然后利用一些先验知识和图像处理方法获得车辆外表面的三维坐标。再根据车辆运动过程中的序列图像,通过图像配准的方法拼接出完整的车辆图像,最后实现车辆外形的三维测量。本文的研究内容主要有:1.本文结合交通应用场景,研究了能够有效利用现场信息的基于消失点的标定算法,实现了相机的非现场标定,并通过实验检验了这种标定方法的准确性。2.本文研究了基于图像获取车辆三维坐标的方法。在基于平面图像的方案中,本文利用车辆到相机的水平距离,及中大型车辆的侧面近似垂直于地面的特点,通过逆投影的方法获取车辆侧面的三维坐标。在基于深度图像的方案中,本文通过深度信息和相机成像的几何关系,获取车辆外表面的三维点云,并通过实验分析了所获取三维坐标的误差及准确性。3.对于数米、甚至十几米的中大型车辆,相机无法通过一帧图像得到完整车辆图像的问题,本文根据车辆在运动过程中的序列图像,研究了车辆图像的配准方法,并提出了一种基于刚体运动一致性的约束方法,有效筛选剔除了错误匹配点,改善了车辆图像配准的结果,并实现了车辆图像的完整拼接。最后,针对完整的带有三维坐标信息的车辆图像,本文介绍了车辆外形测量的具体方法,并通过实验检验了该方法的测量结果,分析了测量误差的主要因素。
[Abstract]:Automatic vehicle recognition is one of the key technologies in its system, and the 3D dimension of vehicle shape can provide a reliable and effective data basis for vehicle recognition. The traditional vehicle shape measurement method mainly depends on the lidar. It has high accuracy, but it is easy to be affected by the weather and atmosphere, and the cost is high, which is not conducive to large-scale practical application. In this paper, a method of vehicle shape measurement based on image processing is studied based on the traffic application scene. The length, width and height of vehicle shape are measured by image processing technology. Among them, for different application scenarios, this paper proposes two implementation schemes based on plane image and depth image, both of which obtain clear vehicle images by using the method of camera close installation. Then some prior knowledge and image processing method are used to obtain the three-dimensional coordinates of the outer surface of the vehicle. According to the sequence images of vehicle motion, the complete vehicle images are stitched up by image registration method, and finally the 3D measurement of vehicle shape is realized. The main contents of this paper are as follows: 1. In this paper, the vanishing point based calibration algorithm which can effectively utilize the field information is studied, and the off-site calibration of the camera is realized. The accuracy of this calibration method is verified by experiments. In this paper, the method of obtaining three-dimensional coordinate of vehicle based on image is studied. In the scheme based on plane image, using the horizontal distance from vehicle to camera and the characteristic that the side of medium and large vehicle is approximately perpendicular to the ground, the 3D coordinate of vehicle side is obtained by inverse projection. In the scheme based on depth image, the 3D point cloud on the external surface of the vehicle is obtained by the geometric relation between the depth information and the camera imaging, and the error and accuracy of the obtained 3D coordinate are analyzed by experiments. For medium and large vehicles with several meters or even more than ten meters, the camera can not get a complete vehicle image through a frame image. In this paper, the registration method of vehicle image is studied according to the sequence image of the vehicle in the process of motion. A constraint method based on rigid body motion consistency is proposed, which can effectively filter out the wrong matching points, improve the result of vehicle image registration, and realize the complete mosaic of vehicle images. Finally, aiming at the complete vehicle image with 3D coordinate information, this paper introduces the specific method of vehicle shape measurement, and tests the results of the method through experiments, and analyzes the main factors of measurement error.
【学位授予单位】：长安大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：U495;TP391.41

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