智能车辆视觉环境感知技术的研究
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摘要
智能车辆是智能交通系统的重要组成部分,其研究的主要目的在于降低日趋严重的交通事故发生率,提高现有道路交通安全和运输效率,在某种程度上缓解能源消耗和环境污染等问题。该技术的研究日益受到国内外学者的关注。
本文针对智能车辆技术中的环境感知问题,对采用视觉技术的车道线检测、车辆检测与跟踪、以及行人检测问题进行了较深入的研究,具体研究内容归纳如下:
(1)一种改进的自适应加权正则化迭代图像复原算法。该算法能够自适应地选择并自动修正正则化参数。在每一步迭代中,不断更新正则化参数并进行复原滤波,使复原结果能够快速趋向于最优,增强了复原过程对强噪声的适应性;一种基于信息测度和支持向量机的图像去噪方法。算法仅对图像中的退化象素进行复原滤波,而未退化的象素保持不变,能够在消除噪声的同时,保存尽可能多的图像细节。
(2)一种面向结构化道路的单目视觉的车道线实时检测方法。首先用Canny边缘检测方法对给定图像进行边缘检测;然后用基于方向优先级的车道线搜索方法,分别对左、右车道独立完成搜索,在增强车道线特征的同时削弱其他边缘特征;接下来用霍夫变换计算每条线段的直线度,过滤具有复杂纹理的边缘;最后利用图像的亮度信息及其变换识别出车道线。对提出方法进行了实验验证,结果显示提出方法定位准确,在PIII 933MHz CPU上的处理速度达平均每秒13帧。
(3)一种融合多种目标特征的单目视觉车辆检测与跟踪方法。首先利用车辆尾部的结构对称性提取出感兴趣区域,减少搜索范围;再利用车辆底部的阴影特征,在感兴趣区域中搜寻车辆可能出现的位置,找出假设目标;然后利用亮度和轮廓信息对假设目标进行对称性验证,排除虚假目标;融合颜色和梯度方向建立目标特征模型,利用均值平移算法在随后的图像序列中对目标进行快速跟踪定位;检测与跟踪联合工作在一种互动机制下,大幅改善了算法的有效性和实时性。实验结果显示提出方法的正确识别率为96%,平均处理速度达每秒24帧。
(4)一种基于小波分形特征的行人检测方法。首先用哈尔小波基将图像分解为不同分辨率下的小波子模式;然后在小波域中选出适当分辨率的小波子模式,对每一个小波子模式都构造出相应尺度的小波分形特征,将这些由小波分形特征组成的特征向量用于训练支持向量机分类器。用戴姆勒克莱斯勒公司提供的测试平台进行了测试,实验结果显示:该方法较现有方法的特征表达简洁、识别效率高。
(5)为配合实车试验工作的开展,试制了智能车辆辅助驾驶装置的原理性样机。设计了视频采集、处理与显示硬件电路,基于OpenCV设计了人机交互界面,用具有较强移植性的C/C++程序设计语言实现了本文所提出的算法。基于该样机,对所提出的车道线检测方法、车辆检测与跟踪方法进行了实车试验验证。
综上所述,本文研究了图像复原、车道线检测、障碍物检测与跟踪,并在试制辅助驾驶装置原理性样机的基础上,对车道线检测、车辆检测与跟踪进行了实车试验,其研究结果在相关领域中有较重要的理论意义和工程应用价值。
Intelligent Vehicle (IV) is an important constituent of the Intelligent Transportation System (ITS).The purpose of IV technology is to reduce the growing incidence of traffic accidents and to improve both the road traffic safety and the transport efficiency. To some extent, it can alleviate the energy consumption and environmental pollution. As a result, the IV technologies have been attracting worldwide attention of scholars increasingly.
This paper conducts in-depth discussions on the vision based lane detection, vehicle detection and tracking, pedestrian detection in different road scenes for environmental perception of intelligent vehicles. Concrete content of the research has been summarized as follows:
(1) Based on adaptive regularization architecture, an improved weighted iterative restoration algorithm is presented. It can select the regularization parameter and modify it automatically. In each iteration step, the algorithm updates the parameter and restores a part of the degraded image synchronously and finally gets the optimal image. Experimental results show that the algorithm is robustness under significant noise. A novel approach of the image de-noising using information measure and SVM is proposed. It can improve image quality by recovery corrupted pixels only, and keep good pixels unchanged. The proposed algorithm can achieve better performance both on de-noising and preserve more image detail.
(2) A real-time structural road oriented approach in monocular camera based lane marking detection is presented. Firstly, Canny detection approach is used to obtain the edge map from a given road image. Secondly, a searching method based on orientation-priority is proposed, which reinforces those potential road lines while degrading otherwise edge features. Thirdly, Hough transform is employed to compute the linearity degree of every edge segment and filter the edges of intricate texture. Finally, the lane markings are identified by the pixel intensity of the image and its transform. Experiment results show that the proposed approach can achieve robust and effective localization of lane markings. The approach can run at an average speed of 13 frames per second on a P III 933 MHz CPU, and can meet the requirements of safety and real-time of vehicle driving.
(3) A monocular camera vehicle detection and tracking approach which by fuse multi-cues is proposed. First, the horizontal symmetry of vehicle rear view is utilized to achieve the region of interest (ROI) so as to reduce search area of following process. Then, the sign of underneath shadow is employed to generate hypothetical positions on which potential vehicles maybe present. Following, both image intensity and figure information are combined to use to verify the vertical symmetry of the potential vehicle candidates. Meanwhile, Mean Shift procedure, based on the object feature model of combining color histogram and orientation histogram, is used to search the potential objects between two sequential image frames fast. More important, both detection and tracking work together under an interactive mechanism which can dramatically improve both detection efficiency and real-time. It shows that the proposed approach can achieve 96% true detection rate and run about average 24 frames per second, which validate the security and real-time requirements.
(4) A novel pedestrian detection approach is presented. Firstly, Haar wavelet is employed to transform the input image into its sub patterns of wavelet region with different resolutions. And then, some relevant wavelet sub patterns are selected to compute the wavelet fractal signature in different scales. Next, this wavelet fractal signature is assembled to be a Wavelet Fractal Signature (WFS) vector, which is utilized to training Support Vector Machine classifier. To validate this approach, some experiments based on Daimler’s Pedestrian Detection Benchmark are conducted; the experimental results show that the proposed approach has the advantages of compact feature expression form and higher detection rate than available approach.
(5) An intelligent vehicle driving assistant device for the purpose of real world experimentation is developing. The hardware is designed including video acquire, image proceeding and display. Based on OpenCV (Open source Computer Vision platform), we designed a simple and friendly man-machine interface. The proposed algorithms have been realized by C/C++, and providing both lane departures reminds and prevents collision warning function. A test in practice has been conducted using this device, and the result validates the approaches proposed by this paper.
In summary, this paper focus on the image de-noising, lane detection, obstacle detection and tracking, and based on the self-developed principle prototype of the driving assistance system, conducts a real vehicle tests of lane detection and vehicle detection and tracking, the results contribute practical and heuristic significance to both theory and engineering application in related fields.
本文针对智能车辆技术中的环境感知问题,对采用视觉技术的车道线检测、车辆检测与跟踪、以及行人检测问题进行了较深入的研究,具体研究内容归纳如下:
(1)一种改进的自适应加权正则化迭代图像复原算法。该算法能够自适应地选择并自动修正正则化参数。在每一步迭代中,不断更新正则化参数并进行复原滤波,使复原结果能够快速趋向于最优,增强了复原过程对强噪声的适应性;一种基于信息测度和支持向量机的图像去噪方法。算法仅对图像中的退化象素进行复原滤波,而未退化的象素保持不变,能够在消除噪声的同时,保存尽可能多的图像细节。
(2)一种面向结构化道路的单目视觉的车道线实时检测方法。首先用Canny边缘检测方法对给定图像进行边缘检测;然后用基于方向优先级的车道线搜索方法,分别对左、右车道独立完成搜索,在增强车道线特征的同时削弱其他边缘特征;接下来用霍夫变换计算每条线段的直线度,过滤具有复杂纹理的边缘;最后利用图像的亮度信息及其变换识别出车道线。对提出方法进行了实验验证,结果显示提出方法定位准确,在PIII 933MHz CPU上的处理速度达平均每秒13帧。
(3)一种融合多种目标特征的单目视觉车辆检测与跟踪方法。首先利用车辆尾部的结构对称性提取出感兴趣区域,减少搜索范围;再利用车辆底部的阴影特征,在感兴趣区域中搜寻车辆可能出现的位置,找出假设目标;然后利用亮度和轮廓信息对假设目标进行对称性验证,排除虚假目标;融合颜色和梯度方向建立目标特征模型,利用均值平移算法在随后的图像序列中对目标进行快速跟踪定位;检测与跟踪联合工作在一种互动机制下,大幅改善了算法的有效性和实时性。实验结果显示提出方法的正确识别率为96%,平均处理速度达每秒24帧。
(4)一种基于小波分形特征的行人检测方法。首先用哈尔小波基将图像分解为不同分辨率下的小波子模式;然后在小波域中选出适当分辨率的小波子模式,对每一个小波子模式都构造出相应尺度的小波分形特征,将这些由小波分形特征组成的特征向量用于训练支持向量机分类器。用戴姆勒克莱斯勒公司提供的测试平台进行了测试,实验结果显示:该方法较现有方法的特征表达简洁、识别效率高。
(5)为配合实车试验工作的开展,试制了智能车辆辅助驾驶装置的原理性样机。设计了视频采集、处理与显示硬件电路,基于OpenCV设计了人机交互界面,用具有较强移植性的C/C++程序设计语言实现了本文所提出的算法。基于该样机,对所提出的车道线检测方法、车辆检测与跟踪方法进行了实车试验验证。
综上所述,本文研究了图像复原、车道线检测、障碍物检测与跟踪,并在试制辅助驾驶装置原理性样机的基础上,对车道线检测、车辆检测与跟踪进行了实车试验,其研究结果在相关领域中有较重要的理论意义和工程应用价值。
Intelligent Vehicle (IV) is an important constituent of the Intelligent Transportation System (ITS).The purpose of IV technology is to reduce the growing incidence of traffic accidents and to improve both the road traffic safety and the transport efficiency. To some extent, it can alleviate the energy consumption and environmental pollution. As a result, the IV technologies have been attracting worldwide attention of scholars increasingly.
This paper conducts in-depth discussions on the vision based lane detection, vehicle detection and tracking, pedestrian detection in different road scenes for environmental perception of intelligent vehicles. Concrete content of the research has been summarized as follows:
(1) Based on adaptive regularization architecture, an improved weighted iterative restoration algorithm is presented. It can select the regularization parameter and modify it automatically. In each iteration step, the algorithm updates the parameter and restores a part of the degraded image synchronously and finally gets the optimal image. Experimental results show that the algorithm is robustness under significant noise. A novel approach of the image de-noising using information measure and SVM is proposed. It can improve image quality by recovery corrupted pixels only, and keep good pixels unchanged. The proposed algorithm can achieve better performance both on de-noising and preserve more image detail.
(2) A real-time structural road oriented approach in monocular camera based lane marking detection is presented. Firstly, Canny detection approach is used to obtain the edge map from a given road image. Secondly, a searching method based on orientation-priority is proposed, which reinforces those potential road lines while degrading otherwise edge features. Thirdly, Hough transform is employed to compute the linearity degree of every edge segment and filter the edges of intricate texture. Finally, the lane markings are identified by the pixel intensity of the image and its transform. Experiment results show that the proposed approach can achieve robust and effective localization of lane markings. The approach can run at an average speed of 13 frames per second on a P III 933 MHz CPU, and can meet the requirements of safety and real-time of vehicle driving.
(3) A monocular camera vehicle detection and tracking approach which by fuse multi-cues is proposed. First, the horizontal symmetry of vehicle rear view is utilized to achieve the region of interest (ROI) so as to reduce search area of following process. Then, the sign of underneath shadow is employed to generate hypothetical positions on which potential vehicles maybe present. Following, both image intensity and figure information are combined to use to verify the vertical symmetry of the potential vehicle candidates. Meanwhile, Mean Shift procedure, based on the object feature model of combining color histogram and orientation histogram, is used to search the potential objects between two sequential image frames fast. More important, both detection and tracking work together under an interactive mechanism which can dramatically improve both detection efficiency and real-time. It shows that the proposed approach can achieve 96% true detection rate and run about average 24 frames per second, which validate the security and real-time requirements.
(4) A novel pedestrian detection approach is presented. Firstly, Haar wavelet is employed to transform the input image into its sub patterns of wavelet region with different resolutions. And then, some relevant wavelet sub patterns are selected to compute the wavelet fractal signature in different scales. Next, this wavelet fractal signature is assembled to be a Wavelet Fractal Signature (WFS) vector, which is utilized to training Support Vector Machine classifier. To validate this approach, some experiments based on Daimler’s Pedestrian Detection Benchmark are conducted; the experimental results show that the proposed approach has the advantages of compact feature expression form and higher detection rate than available approach.
(5) An intelligent vehicle driving assistant device for the purpose of real world experimentation is developing. The hardware is designed including video acquire, image proceeding and display. Based on OpenCV (Open source Computer Vision platform), we designed a simple and friendly man-machine interface. The proposed algorithms have been realized by C/C++, and providing both lane departures reminds and prevents collision warning function. A test in practice has been conducted using this device, and the result validates the approaches proposed by this paper.
In summary, this paper focus on the image de-noising, lane detection, obstacle detection and tracking, and based on the self-developed principle prototype of the driving assistance system, conducts a real vehicle tests of lane detection and vehicle detection and tracking, the results contribute practical and heuristic significance to both theory and engineering application in related fields.
引文
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