【作者】 严捷丰；

【导师】 周荷琴；

【作者基本信息】 中国科学技术大学 ， 模式识别与智能系统， 2008， 博士

【摘要】 对交通路况进行监控,实时、准确的获取各种交通参数,是实施智能交通管理的前提。视频监控方法通过分析拍摄的交通图像序列,对交通目标进行检测、识别和跟踪,并对其行为进行分析和判断。与其它检测手段相比,视频监控方式可以同时获得多种重要路况状态信息,有利于实现交通管理的智能化,具有很大的发展潜力。在智能交通视频监控中,无论采用什么解决方案,首先必须能够检测分割出交通场景中的目标。因此,车辆检测与分割方法的研究,在交通视频监控系统中具有重要的意义。车辆的检测一般采用背景差分方法,该方法的性能受到视频图像稳定性和背景估计鲁棒性的影响,需要有快速有效的图像预处理方法、背景建模方法。车辆分割的难点,主要是场景中存在的阴影,以及车辆间的遮挡粘连。本文针对以上问题进行了研究,取得的主要研究成果和创新点如下:(1)为保证背景差分方法的性能,针对摄像机抖动导致的场景运动,提出一种基于三参数模型的快速图像稳定方法,对运动的性质进行判别和校正。该方法模型参数少,求解过程快,能很好满足监控应用中图像稳定的实际需求。在背景估计方面,提出局域灰度分布的概念,基于该特征提出一种混合多模态背景模型,该模型既可以在象素粒度上,也可以在图像块粒度上进行处理,能够自适应交通场景中背景的多模动态性,具有较好的鲁棒性。(2)针对夜晚光照不足时,图像色调偏红,对比度不高的情况,将Retinex图像增强理论与小波分解相结合,提出一种图像融合增强算法,实验表明相对其他几种典型算法,该算法的性能具有明显的优越性。(3)研究了阴影检测问题,针对HSV色彩空间方法误检率高的问题,本文在利用HSV色彩信息的同时,增加梯度特征判据和几何特征判据,提出一种改进算法,该方法能有效减少阴影的误检率。(4)针对遮挡粘连问题,从遮挡关系分解的角度出发,对一种2.5-D车辆描述模型进行了改进,并与二维凸包分割方法相结合,给出了一种解决方案,对跟踪过程中的遮挡问题提出了新的处理策略。(5)研究了位置违章行为的检测问题。根据车辆的2.5-D描述模型,推导出在自遮挡和交互遮挡情况下,车辆整体轮廓、底部轮廓和单一底边的恢复规则。将车辆运动视为其底部在道路平面上二维共面运动,通过分析车辆底部与道路标线的相对位置关系,提出位置违章的两个检测判据和检测算法。更多还原

【Abstract】 Getting accurate real-time traffic parameters is a prerequisite for intelligent traffic management. By analyzing the traffic image sequence, video surveillance method carries out the detection, recognition and tracking of traffic targets, so as to identify and judge their behaviors. Compared with other means, video surveillance can simultaneously collect various important traffic parameters, which profits to achieve intelligent traffic management, so it has a great potential.In intelligent video based traffic surveillance, before tracking and recognition, it is a common requirement for different solutions to detect and segment vehicles in the video images. Therefore, vehicle detection and segmentation methods are the key infrastructure in the video surveillance system.The common way for vehicle detection is the background difference method, but its performance must be ensured by the stability of video images, and the robustness of background estimation. Shadows in the scene and occlusion between different vehicles are two main challenges to the segmentation of vehicles. This paper did in-depth research on these issues and presents the following innovative works:(1) To guarantee the effectiveness of background difference method, this thesis proposed a three-parameter model to correct the camera motion, which achieves rapid image stabilization; and proposed a mixed multi-modal background model, based on local gray-scale distribution, which can adaptive to the multi-mode dynamics in the background of traffic scene.(2) Night images’ quality is poor because of under exposure, lack of even brightness distributing, and with more red pixels .In this thesis a retinex based algorithm combined with discrete wavelet transform fusion algorithm is proposed to enhance the night image. Compare to others, the image visual effects has been improved significantly by this method.(3) For the detection of shadows, this thesis utilized the gradient and geometric characteristics to improve the traditional HSV method; the new algorithm can effectively reduce mistakes.(4) For the occlusion problem, by improving a 2.5-D description model in occlusion resolvability, and integrating 2-D convex hull segmentation algorithm, this thesis proposed a new solution, and presents an new strategy for occlusion processing in object tracking.(5) A novel algorithm is proposed to detect the vehicle’s position violations. Using the 2.5-D description model to analyze self occlusion and mutual occlusion, we propose the rules of occlusion resolvability. Assuming that the vehicle’s bottom and the road marks were coplanar on the road, analyzing the positional relativity of vehicle’s bottom edges and the road marks, this thesis gains two detecting criterions.更多还原