车牌精确定位与实现

车牌识别系统作为自动识别车辆的一种重要形式,是城市智能交通管理系统中不可或缺的重要组成部分,广泛用于路桥、隧道等卡口的自动收费系统、现代住宅小区、停车场、重要机关单位的汽车出入控制、车流监控等重要场合,尤其在高速公路收费系统中,车牌自动识别更是一个重要的环节。车牌定位作为车牌自动识别中的关键步骤,对系统识别率有重要的影响。     

Detection and classification of road signs in natural environments

An automatic road sign recognition system first locates road signs within images captured by an imaging sensor on-board of a vehicle, and then identifies the detected road signs. This paper presents an automatic neural-network-based road sign recognition system. First, a study of the existing road sign recognition research is presented. In this study, the issues associated with automatic road sign recognition are described, the existing methods developed to tackle the road sign recognition problem are reviewed, and a comparison of the features of these methods is given. Second, the developed road sign recognition system is described. The system is capable of analysing live colour road scene images, detecting multiple road signs within each image, and classifying the type of road signs detected. The system consists of two modules: detection and classification. The detection module segments the input image in the hue-saturation-intensity colour space, and then detects road signs using a Multi-layer Perceptron neural-network. The classification module determines the type of detected road signs using a series of one to one architectural Multi-layer Perceptron neural networks. Two sets of classifiers are trained using the Resillient-Backpropagation and Scaled-Conjugate-Gradient algorithms. The two modules of the system are evaluated individually first. Then the system is tested as a whole. The experimental results demonstrate that the system is capable of achieving an average recognition hit-rate of 95.96% using the scaled-conjugate-gradient trained classifiers.     

Embedded real-time speed limit sign recognition using image processing and machine learning techniques

The number of traffic accidents in Brazil has reached alarming levels and is currently one of the leading causes of death in the country. With the number of vehicles on the roads increasing rapidly, these problems will tend to worsen. Consequently, huge investments in resources to increase road safety will be required. The vertical R-19 system for optical character recognition of regulatory traffic signs (maximum speed limits) according to Brazilian Standards developed in this work uses a camera positioned at the front of the vehicle, facing forward. This is so that images of traffic signs can be captured, enabling the use of image processing and analysis techniques for sign detection. This paper proposes the detection and recognition of speed limit signs based on a cascade of boosted classifiers working with haar-like features. The recognition of the sign detected is achieved based on the optimum-path forest classifier (OPF), support vector machines (SVM), multilayer perceptron, k-nearest neighbor (kNN), extreme learning machine, least mean squares, and least squares machine learning techniques. The SVM, OPF and kNN classifiers had average accuracies higher than 99.5 %; the OPF classifier with a linear kernel took an average time of 87 \(\upmu\)s to recognize a sign, while kNN took 11,721 \(\upmu\)s and SVM 12,595 \(\upmu\)s. This sign detection approach found and recognized successfully 11,320 road signs from a set of 12,520 images, leading to an overall accuracy of 90.41 %. Analyzing the system globally recognition accuracy was 89.19 %, as 11,167 road signs from a database with 12,520 signs were correctly recognized. The processing speed of the embedded system varied between 20 and 30 frames per second. Therefore, based on these results, the proposed system can be considered a promising tool with high commercial potential.

     

基于运动模型的道路识别与跟踪算法的研究

自主驾驶与辅助导航是目前国际上研究的热问题,通过对室外行驶车辆上的ＣＣＤ摄像机所采集的长序列立体图象的处理与分析,研究公路汽车自动视觉导航中的道路识别与跟踪问题。     

基于模糊小波神经网络的交通标志识别方法研究

对交通标志进行实时、正确的识别,是车辆自动导航中一个重要方面.该文介绍了一种基于模糊小波神经网络的交通标志识别方法.该方法首先利用不变矩来提取图像特征,然后将特征向量输入模糊小波神经网络进行识别.该网络以小波函数作为模糊隶属函数,将模糊技术与神经网络相结合,利用神经网络实现模糊推理,并可对隶属函数的形状进行实时调整,从而使网络具有更强的学习和自适应能力.实验证明,该方法具有较高的识别精度和速度,在车辆自动导航中具有较高的应用价值.     

基于ITO玻璃的指纹膜识别方法

通过复制真实指纹纹路得到的指纹膜,可成功欺骗被广泛使用的光学指纹识别设备,给基于指纹验证的应用系统带来了巨大的安全隐患.基于指纹膜材料和真实手指导电能力的不同,通过检测真实手指或指纹膜触摸ITO玻璃时产生的微小电流变化,来实现对指纹膜与真实手指的区分,并基于单片机设计了指纹膜识别系统.经过自制的300余个指纹膜和真实手指的多次验证,本系统的识别率高于95％,可准确地区分出指纹膜和真实手指,提高了基于指纹检测的应用系统的安全性.     

基于彩色立体视觉的障碍物快速检测方法

Real-time obstacle detection method is a key technique for machine vision based mobile robot and au-tonomous land vehicle navigation in unstructured environments. In this paper o considering the real-time requirement for stereo matching algorithm, an adaptive color segmentation method for possible obstacle region detection is first developed based on the color feature, and a simple region based stereo matching algorithm of binocular vision for realobstacle recognition is also introduced. Obstacle detection is implemented by combining the road color adaptive seg-mentation method and region based stereovision method. Lots of experiment results show that the proposed approachcan detect obstacle quickly and effectively, and this algorithm is particularly suited for road environments in which the road is relatively flat and of roughly the same color.     

A hybrid system for embedded machine vision using FPGAs and neural networks

This paper presents a hybrid model for embedded machine vision combining programmable hardware for the image processing tasks and a digital hardware implementation of an artificial neural network for the pattern recognition and classification tasks. A number of possible architectural implementations are compared. A prototype development system of the hybrid model has been created, and hardware details and software tools are discussed. The applicability of the hybrid design is demonstrated with the development of a vision application: real-time detection and recognition of road signs.     

基于三维重建的交通流量检测算法

在智能交通系统中 ,道路交通流量信息实时、有效的检测是交通信息系统的关键环节 .固定相机的视频图象检测法具有诸多优点 ,为此 ,提出了一个基于知识的视频图象交通流量检测系统 ,其中 ,车辆的分割和识别是视频检测法的核心 .根据车辆具有较大的运动惯性等运动规律 ,在短时间隔内 ,可以近似认为车辆运动为刚体匀速直线运动 .在这一条件下 ,将刚体上的运动点重投影到道路平面 ,则重投速度与该点的空间位置到路面的高度具有固定的比例关系 .运动特征采用具有较好定位精度的边缘特征 ,并拟合为直线进行运动跟踪匹配 .在识别过程中 ,先假定车辆的模型及其高度 ,然后再根据重投影速度 ,重建车辆的三维空间结构 ,进行基于知识规则的假设校验 .试验结果表明 ,该方法可以较好地解决车辆视频检测中的遮挡、粘连、阴影等情况     

Automatic system for operational traffic monitoring using very-high-resolution satellite imagery

Vehicle detection from very-high-resolution satellite imagery has received increasing interest during the last few years. In this article, we propose an automatic system for operational traffic monitoring using very-high-resolution optical satellite imagery (0.5–0.6 m resolution) of small highways with low traffic density and a range of different illumination conditions, including cloud-shadowed, hazy, and partially cloudy conditions. The proposed system includes cloud and cloud shadow detection, road detection, and vehicle detection, classification, and counting. The main part of the system is vehicle detection, which is constructed using an elliptical blob detection strategy followed by region growing and feature extraction steps. Vehicular objects are separated from non-vehicular objects using a K-nearest-neighbour classifier, with various classical features used for pattern recognition, as well as some proposed application-specific features, and are also classified according to vehicle size. The fully automatic processing chain has been validated on a selection of satellite scenes from different parts of Norway, including imagery with large amounts of cloud, fog, cloud shadows, and similar conditions that complicate image interpretation. The overall vehicle detection rate was 85.4% and the false detection rate was 9.2%. Overall, this demonstrates the potential of operational traffic monitoring using very-high-resolution satellites.     

Estimation of Vehicle Pose and Position with Monocular Camera at Urban Road Intersections

With the rapid development of urban, the scale of the city is expanding day by day. The road environment is becoming more and more complicated. The vehicle ego-localization in complex road environment puts forward imperative requirements for intelligent driving technology. The reliable vehicle ego-localization, including the lane recognition and the vehicle position and attitude estimation, at the complex traffic intersection is significant for the intelligent driving of the vehicle. In this article, we focus on the complex road environment of the city, and propose a pose and position estimation method based on the road sign using only a monocular camera and a common GPS (global positioning system). Associated with the multi-sensor cascade system, this method can be a stable and reliable alternative when the precision of multi-sensor cascade system decreases. The experimental results show that, within 100 meters distance to the road signs, the pose error is less than 2 degrees, and the position error is less than one meter, which can reach the lane-level positioning accuracy. Through the comparison with the Beidou high-precision positioning system L202, our method is more accurate for detecting which lane the vehicle is driving on.     

Embedded omni-vision navigator based on multi-object tracking

This paper presents an embedded omni-vision navigation system which involves landmark recognition, multi-object tracking, and vehicle localization. A new tracking algorithm, the feature matching embedded particle filter, is proposed. Landmark recognition is used to provide the front-end targets. A global localization method for omni-vision based on coordinate transformation is also proposed. The digital signal processor (DSP) provides a hardware platform for on-board tracker. Dynamic navigator employs DSP tracker to follow the landmarks in real time during the arbitrary movement of the vehicle and computes the position for localization based on time sequence images analysis. Experimental results demonstrated that the navigator can efficiently offer the vehicle guidance.     

基于数字图像的汽车驾驶员行驶状态判别

通过车载CCD图像传感器摄取图像,在利用中值滤波、边缘检测等图像处理技术去除噪声和获取道路标线的基础上,建立了摄像机的透视投影模型和汽车驾驶员行驶状态模型,研究了车辆行驶过程中相对道路标线的行驶状态参数,以监控驾驶员行车状况。实验证明,该方法获得的行驶状态参数曲线能有效判别驾驶员的行驶状态,为减少驾驶员人为因素导致的交通事故作了有益的探索。     

Participants increasing for threshold random grids-based visual secret sharing

Reconfigurability and parallel computing capability of field programmable gate array (FPGA) devices are highly exploited in real-time digital image and video processing applications. In this field, real-time traffic road signs detection systems present a huge interest since they help to assist drivers and decrease accidents. In this paper, we propose an FPGA-based hardware implementation of road signs detection and identification system. The proposed system can achieve real-time video constraints while assuring a high-level accuracy in terms of detection rate. The performance of the system in terms of processing latency was evaluated relatively to the reaction distance, the braking distance and the vehicle speed. The evaluation results show that our system can support real-time driving conditions until the speed of 110 km/h. To prove the validity of the proposed implementation, a hardware co-simulation strategy was applied. This is based on the use of Matlab/Xilinx system generator. A comparison of the co-simulation results shows the effectiveness of the developed architecture.     

A decision support system for the automatic management of keep-clear signs based on support vector machines and geographic information systems

This paper presents a decision support system for automatic keep-clear signs management. The system consists of several modules. First of all, an acquisition module obtains images using a vehicle equipped with two recording cameras. A recognition module, which is based on Support Vector Machines (SVMs), analyzes each image and decides if there is a keep-clear sign in it. The images with keep-clear signs are included into a Geographical Information System (GIS) database. Finally in the management module, the data in the GIS are compared with the council database in order to decide actions such as repairing or reposition of signs, detection of possible frauds etc. We present the first tests of the system in a Spanish city (Meco, Madrid), where the systems is being tested for its application in the near future.     

车牌识别系统中边缘检测及算法研究

车牌自动识别检测是智能交通系统的重要组成部分,具有十分广阔的应用前景。车牌图像预处理是在汽车牌照识别系统的一个重要环节,预处理性能对车牌识别检测具有重要作用。介绍了几种常用的边缘检测算子,不同敏感性不同,验证了不同类型的边缘提取算法,采用不同的边缘敏感算子进行检测。     

基于计算机视觉和图像处理的交通参数检测

本文提出了一种基于计算机视觉和图像处理的车流量检测方法,通过分析CCD TV 摄 像机获取的图像中车辆和场景信息来有效地检测交通参数．本方法可以完成双向四车道高速 公路和城市道路的车辆计数和车辆速度检测．实验结果表明：车辆计数的正确率为94％,车 辆速度检测的正确率达92%,并具有满意的实时特性．     

The Warp machine on Navlab

The authors review the history of the Carnegie-Mellon Warp machine on Navlab, an autonomous land vehicle, and describe three Navlab vision systems implemented on the Warp machine. They then critically evaluate components of Warp in light of this experience. The Warp machine was used to implement stereo vision for obstacle avoidance and color-based road-following systems. The stereo-vision system was FIDO, which is derived from some of the earliest work in vision-guided robot vehicle navigation. Two color-based road following systems were implemented; one adapted conventional vision techniques to the problem of road recognition and the other used a neural network-based technique to learn road following online. Finally, the authors discuss the value of applications integration with machine development, discuss the limitations of the attached processor model, and give recommendations for future systems     

基于嵌入式控制系统的视觉导引自动小车设计

以嵌入式系统为核心控制器,设计了基于视觉导引的自动小车系统。采用模块化硬件设计,分别开发了运动控制、步进电机移载、视觉导引、自动充电、安全避障、无线通信等各种硬件模块。研究了基于视觉的路径特征提取与识别,设计了分区导引控制算法实现小车自动循迹前进。在Linux操作系统环境下开发了控制系统软件和远程监控软件,最后完成了系统测试与调试。     

汽车驾驶仿真虚拟环境中路面标示的智能化建模

高速公路以及城市道路路面上的各种标示是汽车驾驶员的重要指示符号，因而它们是汽车驾驶仿真场景中的重要组成部分。但是，路面上的标示在三维空间中受路面的几何约束和它们间的连接约束，并且高速公路以及城市道路的路面又不总是笔直平面的，这些因素导致按普通的绘图方法在路面上绘制各种标示是费时的、不能满足汽车驾驶仿真应用中对场景实时建模的要求。所以，该文分析了汽车驾驶仿真虚拟环境中路面上的车道线和其它标示的绘制特点。按它们所受的几何约束进行了分类，进而提出了基于JAVA3D和面向对象设计方法的智能化绘制路面标示的关键技术。用该技术所开发的场景建模器能自动连接和调整路面标示以满足约束关系．达到使建模人员方便快速建模路面标示的目的．