题名 | 路面交通场景中的车辆定位与识别 |
作者 | 张兆翔![]() |
学位类别 | 工学博士 |
答辩日期 | 2009-09-03 |
授予单位 | 中国科学院研究生院 |
授予地点 | 中国科学院自动化研究所 |
导师 | 谭铁牛 |
关键词 | 三维模型 物体识别 物体分类 物体定位 物体跟踪 拟合度评估 演化计算 摄像机标定 3D Model Object Recognition Object Classification Object Localization Object Tracking Fitness Evaluation Evolutionary Computing Camera Calibration |
其他题名 | 3D Model Based Vehicle Localization and Recognition in Traffic Scene Surveillance |
学位专业 | 模式识别与智能系统 |
中文摘要 | 基于三维模型的物体识别是计算机视觉领域的一个经典问题。将三维模型作为先验知识应用于物体识别问题中,可以降低图像底层处理的复杂度;增强算法对噪声、遮挡等干扰的鲁棒性;从图像平面获得真实的三维数据。本文以路面交通场景中的智能视觉监控为应用背景,对基于三维车辆模型的车辆定位与识别问题展开了深入的研究,提出了一系列新颖的算法,取得了较好的性能。本文的主要工作有: 1. 摄像机模型获取(即摄像机标定)是基于三维模型的物体识别的先决条件。针对这一需求,提出一种基于路面交通场景视频中运动目标的表象和运动信息的摄像机标定方法。该方法仅需要摄像机高度信息作为唯一的用户输入,即可完全恢复摄像机的内外参数,搭建三维模型和二维图像数据之间关联的桥梁。该方法在最大程度上减少了人工测量的工作量,避免了附加几何结构的参与,改善了基于三维模型的物体识别方法的推广性。 2. 视频中的运动目标分类是基于三维模型的物体识别的预处理步骤。针对这一需求,提出两种无监督在线学习的运动目标分类方法,均可以有效处理摄像机投影所带来的透视变形。通过运动目标分类,可以检测出图像中感兴趣的车辆区域,初始化车辆的三维姿态,缩小定位与识别算法的搜索空间。 3. 模型投影与图像数据之间的拟合度评估是基于三维模型的物体识别的核心问题。针对这一需求,提出一种基于图像局部梯度信息的拟合度评估方法。该方法不需要图像平面上几何基元的提取和距离计算,具有高效性和准确性。该方法所提供的良好的优化曲面性质,为后续工作的开展提供了便利。 4. 基于三维模型的车辆定位、跟踪与识别是拟合度评估方法的具体应用。在车辆定位中,应用梯度下降法进行拟合度优化,获得车辆的准确三维姿态。在车辆跟踪中,将拟合度评估与粒子滤波器跟踪框架相结合,取得了较好的跟踪效果。在车辆识别中,讨论了基于固定车辆模型的物体识别策略,分析了基于固定模型方法的局限性。 5. 在基于固定三维模型的物体识别基础上,对物体模型进行了扩展,提出了可形变的参数化车辆模型。在优化策略上,提出了基于演化计算的迭代搜索框架,从图像数据中恢复车辆的三维姿态参数和形状参数。姿态参数被用来进行车辆定位;形状参数被用来实现车辆识别。 |
英文摘要 | 3D model based object recognition is a classical problem in computer vision. The prior information provided by the 3D model can not only simplify the low level process-ing of image data and improve the robustness to noise and occlusions, but also make it possible to recover 3D information directly from image plane. In this dissertation, we focus on the 3D model based vehicle localization and recognition in traf.c scene surveillance. Novel algorithms are proposed and experimental results demonstrate the performance of these approaches. The major contributions of the dissertation include the following: 1. Camera calibration is the indispensable step of 3D model based object recogni-tion, which establishes the relations between the 3D model and image data. We propose a camera calibration method based on the appearance and motion infor-mation of objects in traf.c scene surveillance videos. With the camera height measured as the only user input, our method can completely recover both the in-trinsic and extrinsic parameters of the camera. Our practical camera calibration method greatly improves the applicability of 3D model based recognition. 2. Automatic object classi.cation in videos is the preprocessing step of 3D model based object recognition. We propose two object classi.cation methods to deal with perspective distortions of 2D image features. Both methods are based on online learning and avoid the acquisition of large database and manual labeling. Object classi.cation makes it possible to extract vehicle regions of interest from image plane so that we can initialize the 3D pose of vehicles and reduce the search space. 3. Fitness evaluation between the projection of 3D model and image data is the key problem in 3D model based object recognition. We propose a .tness evaluation method based on local gradient information of image data. The method avoids 2D geometric primitive extraction and distance calculation to show outstanding accuracy and ef.ciency. The good properties of the optimization surface offer convenience to the following .tness optimization. 4. The 3D model based vehicle localization, tracking and recognition can be seen as applications of .tness evaluation. We make use of gradient decent for .tness optimization to achieve 3D model based vehicle localization. Furthermore, we combine the .tness evaluation with the particle .lter based tracking framework, which shows good performance with robustness to noise. At last, we investigate the s... |
语种 | 中文 |
其他标识符 | 200618014628065 |
内容类型 | 学位论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/6226] ![]() |
专题 | 毕业生_博士学位论文 |
推荐引用方式 GB/T 7714 | 张兆翔. 路面交通场景中的车辆定位与识别[D]. 中国科学院自动化研究所. 中国科学院研究生院. 2009. |
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