基于盲源分离的车辆检测与分类技术研究
发布时间：2018-11-02 15:31
【摘要】：智能交通系统近些年得到快速发展,其关键技术是运动车辆的检测与分类。基于压力传感器等传统的检测与分类技术存在安装、维护成本高昂等缺点,而基于视频图像的车辆检测与分类技术能克服上述缺点。现有基于视频的分类技术主要归结为四类:车牌识别法、基于车辆几何特征分类法、基于车辆外形轮廊匹配法与基于PCA和LDA的代数特征法。车牌识别法受车牌信息库与遮挡车牌情况的限制;几何特征(长,宽,高等)分类准确率低下,只适用于粗略分类;基于提取外形轮廊进行模板匹配的方法,实际应用中往往难以提取到完整轮廊,导致分类准确率不高,并且只适用于轮廊信息明显的侧面视角监控视频;基于图像低阶信息的代数特征在应用上简单,且不受监控视频视角的影响,但同样存在分类准确率不高的问题。车辆分类是交通监控系统的关键,本文将盲源分离算法引入到车辆分类问题的研究中,利用ICA能够去除信号高阶统计相关性的特点进行车辆图像的特征提取。通过实验分析ICA的两种结构所提取得到的车辆特征在分类性能上的优劣,实验验证ICA模型提取的车辆特征在分类性能上要优于传统的PCA与LDA算法。在此基础上,提取车辆的几何特征(长与宽的和)并根据车辆在图像中的位置对该特征进行修正,并将该特征用于对车型大小进行预分类,结合代数特征,构建二次分类系统。基于几何特征与代数特征的二次分类系统,能进一步提升车辆分类的准确率。运动车辆的检测是分类的基础,本文针对道路监控视频特点,对三帧差法进行改进。针对运动车辆表面颜色比较均匀一致或者车身颜色与背景路面颜色相近的情况,传统三帧差法无法提取到完整的车辆图像区域,改进的三帧差法对这些情况具有更好的适应性并保持了运行速度快的优点,能满足实时性的要求。在运动检测中,基于正面视角的道路监控视频,前后车距较小时检测会误将两辆车当作一辆车,提出使用形态学腐蚀算子进行粘连车辆的分离,通过定位两辆车的中心进行车辆图像分割。本文初步设计并实现了一个基于OpenCV与VS2010的道路监控系统,该系统可以实现运动车辆的检测、跟踪、计数与分类功能。
[Abstract]:Intelligent Transportation system (its) has been developing rapidly in recent years. The key technology of its is the detection and classification of moving vehicles. The traditional detection and classification technology based on pressure sensor has some disadvantages such as installation high maintenance cost and so on. However vehicle detection and classification technology based on video image can overcome these shortcomings. The existing video classification techniques can be divided into four categories: license plate recognition method, vehicle geometric feature classification method, vehicle profile wheel-corridor matching method and algebraic feature method based on PCA and LDA. The method of license plate recognition is limited by the information base of license plate and occlusion of license plate, the accuracy of geometric feature (length, width, high etc.) is low, so it is only suitable for rough classification. Based on the method of template matching, it is difficult to extract the complete wheel corridor in practical application, which leads to the low classification accuracy and is only suitable for the profile visual angle surveillance video with obvious profile information. The algebraic feature based on low order information of image is simple in application and not affected by the visual angle of surveillance video, but it also has the problem of low classification accuracy. Vehicle classification is the key of traffic monitoring system. In this paper, blind source separation algorithm is introduced into the study of vehicle classification problem, and the feature extraction of vehicle image can be carried out by using ICA which can remove the high order statistical correlation of signal. The classification performance of the vehicle features extracted from the two structures of ICA is analyzed experimentally. The experimental results show that the vehicle features extracted by the ICA model are superior to the traditional PCA and LDA algorithms in classification performance. On this basis, the geometric feature (the sum of length and width) of the vehicle is extracted and modified according to the position of the vehicle in the image. The feature is used to pre-classify the vehicle size and the quadratic classification system is constructed by combining the algebraic features. The quadratic classification system based on geometric and algebraic features can further improve the accuracy of vehicle classification. The detection of moving vehicles is the basis of classification. According to the characteristics of road surveillance video, the three frame difference method is improved in this paper. When the surface color of moving vehicle is uniform or the color of the body is similar to that of the background road, the traditional three-frame difference method can not extract the complete vehicle image area. The improved three-frame difference method has better adaptability to these situations and keeps the advantage of fast running speed, which can meet the requirement of real-time. In the motion detection, the road surveillance video based on the positive angle of view, when the distance between the two vehicles is small, will be mistaken as a vehicle, and the morphological corrosion operator is used to separate the adhesion vehicle. The vehicle image is segmented by locating the center of the two vehicles. In this paper, a road monitoring system based on OpenCV and VS2010 is designed and implemented. The system can detect, track, count and classify moving vehicles.
【学位授予单位】：电子科技大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：U495;TP391.41
[Abstract]:Intelligent Transportation system (its) has been developing rapidly in recent years. The key technology of its is the detection and classification of moving vehicles. The traditional detection and classification technology based on pressure sensor has some disadvantages such as installation high maintenance cost and so on. However vehicle detection and classification technology based on video image can overcome these shortcomings. The existing video classification techniques can be divided into four categories: license plate recognition method, vehicle geometric feature classification method, vehicle profile wheel-corridor matching method and algebraic feature method based on PCA and LDA. The method of license plate recognition is limited by the information base of license plate and occlusion of license plate, the accuracy of geometric feature (length, width, high etc.) is low, so it is only suitable for rough classification. Based on the method of template matching, it is difficult to extract the complete wheel corridor in practical application, which leads to the low classification accuracy and is only suitable for the profile visual angle surveillance video with obvious profile information. The algebraic feature based on low order information of image is simple in application and not affected by the visual angle of surveillance video, but it also has the problem of low classification accuracy. Vehicle classification is the key of traffic monitoring system. In this paper, blind source separation algorithm is introduced into the study of vehicle classification problem, and the feature extraction of vehicle image can be carried out by using ICA which can remove the high order statistical correlation of signal. The classification performance of the vehicle features extracted from the two structures of ICA is analyzed experimentally. The experimental results show that the vehicle features extracted by the ICA model are superior to the traditional PCA and LDA algorithms in classification performance. On this basis, the geometric feature (the sum of length and width) of the vehicle is extracted and modified according to the position of the vehicle in the image. The feature is used to pre-classify the vehicle size and the quadratic classification system is constructed by combining the algebraic features. The quadratic classification system based on geometric and algebraic features can further improve the accuracy of vehicle classification. The detection of moving vehicles is the basis of classification. According to the characteristics of road surveillance video, the three frame difference method is improved in this paper. When the surface color of moving vehicle is uniform or the color of the body is similar to that of the background road, the traditional three-frame difference method can not extract the complete vehicle image area. The improved three-frame difference method has better adaptability to these situations and keeps the advantage of fast running speed, which can meet the requirement of real-time. In the motion detection, the road surveillance video based on the positive angle of view, when the distance between the two vehicles is small, will be mistaken as a vehicle, and the morphological corrosion operator is used to separate the adhesion vehicle. The vehicle image is segmented by locating the center of the two vehicles. In this paper, a road monitoring system based on OpenCV and VS2010 is designed and implemented. The system can detect, track, count and classify moving vehicles.
【学位授予单位】：电子科技大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：U495;TP391.41
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