交通场景下基于视频的智能监控分析关键技术研究

项目名称： 交通场景下基于视频的智能监控分析关键技术研究

项目编号： No.61502119

项目类型： 青年科学基金项目

立项/批准年度： 2016

项目学科： 其他

项目作者： 郑媛

作者单位： 内蒙古大学

项目金额： 21万元

中文摘要： 随着交通监控范围的扩大，传统的人工监控方法已经不能满足当前交通监控的需求。智能交通监控利用计算机完成各种监控任务，是交通监控发展的必然趋势。本课题重点研究智能交通监控的两个关键问题：道路相机标定和车辆3D-2D匹配。针对现有的道路相机标定方法存在所能估计的参数个数少或标定条件苛刻的问题，拟研究两种解决方案：基于消失点和消失线的标定方法和基于车辆信息的标定方法。第一种方案中引入水平消失线到标定条件中，与两个消失点组成最小标定条件，并将迭代重加权策略应用到基于最小标定条件的标定方法中，以提高标定方法的实用性和准确性；第二种方案中引入车辆作为标定物，将道路相机标定问题转化为车辆匹配问题，适用于各种交通场景。针对现有的车辆3D-2D匹配方法易受遮挡、干扰影响的问题，拟研究一种融合局部特性和全局特性的车辆3D-2D匹配方法。本课题的开展将为智能交通监控的研究提供坚实的理论基础和有效的技术支撑。

中文关键词： 交通监控；道路相机标定；车辆3D-2D匹配

英文摘要： With the development of society and economy, the range of traffic monitoring is expanding. As a result, traditional manual monitoring approaches no longer meet the needs of the current traffic monitoring systems. Integrating video analysis methods, intelligent traffic surveillance technology employs computers to complete the various monitoring tasks and is the inevitable trend of the development of traffic monitoring. In this project, we focus on roadside camera calibration problem and 3D-2D vehicle matching, which are two key problems in intelligent traffic surveillance. Considering that in the existing roadside camera calibration methods the number of the parameters to be estimated is relatively small or the used calibration condition is too strict to satisfy, we will present two solutions: the calibration method based on vanishing point and vanishing line and the calibration method based on vehicle information. In the first solution, we will present a detector of the minimum calibration condition which consists of two vanishing points and a vanishing line, and then apply iterative reweighted least squares to the minimum-calibration-condition-based calibration method to improve its practicality and accuracy. In the second solution, vehicle model is introduced as a new calibration pattern and the camera calibration problem is converted into a vehicle matching problem. Therefore, the vehicle-information-based calibration method is suitable for a variety of traffic scenarios. Considering that the existing vehicle matching methods often poorly perform when the clutter or occlusion exists, we will propose a new 3D-2D vehicle matching method that takes into account both local and global characteristics of vehicle matching. This project is to provide a solid theoretical foundation and an effective technical support for intelligent traffic monitoring.

英文关键词： traffic surveillance;roadside camera calibration; 3D-2D vehicle matching