车辆遮挡检测的研究与应用

【作者】 肖建军；

【导师】 苏志同； 熊昌镇；

【作者基本信息】 北方工业大学 ， 计算机应用与技术， 2010， 硕士

【摘要】 运动车辆检测是智能交通领域应用研究中关键的一环,是获得交通信息的基础和保证。但是检测过程中发生的车辆遮挡严重影响了车辆的统计。为了更好进行运动车辆检测,遮挡检测成为一个必不可少的环节。本文针对高清图像的车辆检测,首先对图像预处理方法进行了研究,并通过设定感兴趣区域,去除干扰区域减少算法计算量。然后研究了现有的各种前景检测算法,比较各种算法的优劣性,并在此基础上提出了自己的前景检测算法。通过本文的算法,可以较快的从高清图像中准确的提取出车辆,在前景检测算法的基础上,实现更精确的车辆轮廓提取,为接下来车辆遮挡检测打下了很好的基础。论文的主要工作如下：(1)图像的获得与预处理方面：摄像头的架接,图像的获取,图像的预处理,比较边缘检测算法,选择出一种能很好提取车辆边缘的边缘检测算法。(2)前景检测方面：针对高清图像,提出一种在时间和效果上都比较理想的前景检测算法,解决了现有背景建模算法造成的车辆空洞和断层。(3)车辆遮挡方面：在前景检测的基础上展开车辆遮挡检测研究,对于部分车辆遮挡,利用凸包计算出是否发生遮挡,并根据凸包差来对遮挡车辆进行分割。对于严重遮挡时车辆信息不全的特点,计算获得车辆角点,通过计算角点的光流来判断车辆是否发生遮挡,并根据相同运动向量,划分角点区域来处理车辆遮挡。(4)软件系统方面：根据前面的算法研究,设计车辆图像遮挡检测的整个流程。设计各个模块、算法流程以及具体的实现方法,以便更加直观的体现出算法的效果。通过大量高清图像进行测试,比较多种算法效果,从而不断对本文算法进行改进。最终结果表明新的前景检测算法计算量小而且效果好,遮挡检测算法能快速、有效的检测出车辆遮挡并处理。更多还原

【Abstract】 It is the most important part to extract moving vehicles in application of intelligent transportation field, which is the basis and guarantee to obtain traffic information. But the vehicle occlusion occurred during detection seriously affect the vehicle’s statistics. In order to get the moving vehicle detection, vehicle occlusion detection is as an essential link.In vehicles detection of high-definition images, first of all, this article study on the image pre-processed method, and remove interference region to reduce calculated quantities by setting regions of interest. Then compare the advantages and disadvantages of various algorithms after the study of various existing detection algorithms. And put forward a method for prospect detection. Using this algorithm, you can extract vehicles fast and accurately from high definition images and achieve a more accurate contour extraction on the basis of prospect detection, which lay a good foundation for the next vehicle occlusion detection.The main work in this thesis is as follows:(1) Image acquisition and preprocessing: the received of camera, image acquisition, image preprocessing, compare of edge detection algorithms, the chosen of a good edge detection algorithm to extract the edge of vehicles.(2) Prospect detection: propose a method for prospect detection in high-definition image which is ideal both in time and effect, it can solve the empty and chasm caused by the existing background modeling algorithms background modeling algorithm.(3) Vehicle occlusion: use the convex hull to decide whether occlusion occurred or not in partial occlusion on the basis of prospect detection, and divide occlusion vehicles by the difference of convex hull. For serious occlusion, vehicle information is incomplete, we obtain vehicle comers by calculation, and calculate the optical flow of comer points to determine the occurrence of occlusion, then according to the same motion vector, handle vehicle occlusion by dividing the region of comer.(4) Software system: according to the previous algorithm, design the entire process of vehicle occlusion detection. Design every module, the algorithm’s process and specific method of implementation, which can reflect a more intuitive algorithm result.We continuously improve this algorithm by testing a large number of high-definition images and comparing the effect of variety of algorithms. Final results show that the new prospect detection algorithm has the virtue of less computation and good effect. Occlusion detection algorithm can detect and deal with vehicle occlusion quickly and effectively.更多还原