基于多特征融合的道路交通标志检测

在道路交通标志的检测中,针对自然实景情况中拍摄到的图像存在的交通标志大小和位置不确定等困难问题,本文提出一种基于实景图像的多特征融合的道路交通标志检测方法。论文把样本分为了训练和测试样本,首先对训练样本图像进行盲复原处理;其次对复原处理后的图像进行自适应性的形状区域裁剪,提取裁剪区域图像的颜色、纹理和形状特征;再次分别对颜色、纹理和形状特征进行SVM分类检测,从而获得颜色、纹理和形状三个分类模型;最后对模型的权值进行自适应性计算,得到加权的特征融合模型。通过测试样本对模型的检测,结果表明特征融合识别方法有很高的准确度,另外对比实验得到的对比数据显示融合模型提高了道路交通检测的准确度和鲁棒性。      

基于颜色空间和模板匹配的交通标志检测方法

交通标志检测技术是交通标志识别系统的重要前提和基础。由于背景的复杂性,在进行颜色分割时部分区域可能会受到干扰,为交通标志的检测带来困难。文中利用HSV颜色空间和RGB颜色空间对不同颜色的交通标志进行粗检测,标记不同的值实现ROI分割。然后利用模板匹配的方法对交通标志进行处理,使用模板在ROI区域上滑动,得到模板相似度的最大值,以此来实现检测过程。实验结果表明该方法能获得较好的检测结果。     

模拟路标识别及其FPGA实现

针对路标信号的多变性给驾驶员行车途中带来诸多隐患,提出了基于FPGA辅助驾驶中的模拟路标识别系统,该系统实现了实时检测和识别路标信号,并将识别结果显示出来,以便驾驶员做出相应的响应,在一定程度上维护了交通安全。实验结果表明,基于硬件编程语言Verilog设计的硬件电路可快速、稳定地对图像进行二值化处理,且该系统可实现识别向左、向右、禁止停车、禁止通行4种路标,对辅助驾驶系统的研究具有一定的意义。     

城市道路指路标志的微观仿真研究

道路指路标志能够为驾驶员提供到达目的地的指示信息.目前,道路指路标志布局的合理性评估尚缺乏有效的工具.文章基于微观交通仿真原理,研究了道路指路标志布局的评估方法.论文给出了道路指路标志的数据结构表示,进行了道路指路标志的交通场景虚拟构建,提出了基于指路标志启发信息的驾驶员路径选择行为的建模方法,实现了城市道路指路标志布...     

层次轮廓计算机视角的交通道路标识别

随着我国科学技术的进一步优化,计算机视觉应用范围变得越来越广,尤其是交通道路路标识别中。但面对现阶段计算机视觉对交通路标识别存在的复杂性与不稳定性等问题,随之提出了通过图像轮廓识别技术运用的解决方法。同时,文章也将针对层次轮廓计算机视角的交通道路标识别进行研究,进而保证路标识别上的稳定性。     

自然环境下交通标志牌的检测与识别

在社会经济飞速发展背景下,汽车的应用得到普及,已进入千家万户,汽车为人们出行带来了便利性,不过频发的交通事故也值得深思。而引起交通事故的原因之一即未及时检测到交通标志牌,故而探索出可快捷进行交通标志牌检测与识别的技术非常重要,该检测结合智能交通技术进行交通标志识别系统的设计,设计的识别方法即经提取标准图片与待识别图片的垂直、水平方位的灰度分布,完成Pearson相关系数计算工作,并通过一定阈值的设定,于待识别图片和所有标准图片里均作Pearson相关系数计算后,相关系数的值为最大,同时比阈值更大则认定成匹配成功来达到提醒与警示驾驶员的作用,将交通事故发生率控制到最低。     

基于感兴趣区域和HOG-MBLBP特征的交通标识检测

交通标识检测中样本类别间的不平衡常常导致分类器的检测性能弱化,为了克服这一问题,该文提出一种基于感兴趣区域和HOG-MBLBP融合特征的交通标识检测方法。首先采用颜色增强技术分割提取出自然背景中交通标识所在的感兴趣区域;然后对标识样本库提取HOG-MBLBP融合特征,并用遗传算法对SVM交叉验证进行参数的优化选取,以此来训练和提升SVM分类器性能;最后将提取的感兴趣区域图像的HOG-MBLBP特征送入训练好的SVM多分类器,进行进一步的精确检测和定位,剔除误检区域。在自建的中国交通标识样本库上进行了实验,结果表明所提方法能达到99.2%的分类准确度,混淆矩阵结果也表明了该方法的优越性。     

基于颜色和边缘信息的交通标志检测

根据我国交通标志的颜色和几何属性,提出了一种适用于自然背景下的交通标志检测方法。该方法采用RGB彩色分量差对自然背景下的禁令标志图像进行分割,结合最小二乘法对像素坐标进行椭圆拟合,再根据边界的圆形度参数判断是否为圆形区域。实验证明了该方法的有效性与鲁棒性。     

Road-sign detection and tracking

In a visual driver-assistance system, road-sign detection and tracking is one of the major tasks. This study describes an approach to detecting and tracking road signs appearing in complex traffic scenes. In the detection phase, two neural networks are developed to extract color and shape features of traffic signs from the input scenes images. Traffic signs are then located in the images based on the extracted features. This process is primarily conceptualized in terms of fuzzy-set discipline. In the tracking phase, traffic signs located in the previous phase are tracked through image sequences using a Kalman filter. The experimental results demonstrate that the proposed method performs well in both detecting and tracking road signs present in complex scenes and in various weather and illumination conditions.     

基于尾灯跟踪的夜间车辆检测

夜间车辆检测在城市交通检测技术中有着重要的研究意义。夜晚尾灯的彩色信息作为车辆的一个显著标志,能帮助我们比较准确地检测到车辆。因此,提出了一种基于尾灯跟踪的夜间车辆检测方法。先通过HSV颜色模型对尾灯的颜色信息进行分割,辨认出车辆位置,再通过区域边界锁定车灯边缘信息对车辆进行跟踪。实验证明,该方法提高了检测效率,减少了白色路灯干扰,比较有效地检测到行驶车辆。     

APPOS: An adaptive partial occlusion segmentation method for multiple vehicles tracking

In traffic surveillance videos, it is common that the vehicles are occluded partially by each other. Such kind of occlusion situation is a challengeable task in multiple vehicles tracking. Various solutions in dealing with the occlusion for vehicles tracking have been proposed in many literatures. However, most of them are specialized on one tracking method and cannot flexibly adapt to the others. In this paper, we propose an adaptive partial occlusion segmentation method (APPOS) for multiple vehicles tracking. In this method, the occlusion detection process is firstly conducted to discover the occlusion. After that, the candidate regions of the respective occluded vehicles are roughly evaluated by the contour’s optical flow. Finally, the line scanning which uses color contrast among regions is adopted to accurately locate the vehicles. We evaluate the effectiveness and accuracy of APPOS by the experiments on practical and simulating videos.     

Automatic detection and recognition of signs from natural scenes

In this paper, we present an approach to automatic detection and recognition of signs from natural scenes, and its application to a sign translation task. The proposed approach embeds multiresolution and multiscale edge detection, adaptive searching, color analysis, and affine rectification in a hierarchical framework for sign detection, with different emphases at each phase to handle the text in different sizes, orientations, color distributions and backgrounds. We use affine rectification to recover deformation of the text regions caused by an inappropriate camera view angle. The procedure can significantly improve text detection rate and optical character recognition (OCR) accuracy. Instead of using binary information for OCR, we extract features from an intensity image directly. We propose a local intensity normalization method to effectively handle lighting variations, followed by a Gabor transform to obtain local features, and finally a linear discriminant analysis (LDA) method for feature selection. We have applied the approach in developing a Chinese sign translation system, which can automatically detect and recognize Chinese signs as input from a camera, and translate the recognized text into English.     

交通警告标志定位方法研究

结合交通标志的彩色图像色彩特征和形状特征,在CCD摄像机采集的场景图像中对三角形交通警告标志定位方法进行研究.先利用RGB到HSV的彩色空间转换和阈值分割将交通标志区域信息增强,然后利用边缘检测提取交通标志的三角形形状特征,并对三个顶点进行准确检测,最后通过直线拟合的方法用三条红色直线标示交通标志的精确位置.实验结果表明,应用上述综合设计能对图像中的三角形交通标志进行准确定位和位置标示.     

An efficient dynamic traffic light scheduling algorithm considering emergency vehicles for intelligent transportation systems

Traffic lights have been installed throughout road networks to control competing traffic flows at road intersections. These traffic lights are primarily intended to enhance vehicle safety while crossing road intersections, by scheduling conflicting traffic flows. However, traffic lights decrease vehicles’ efficiency over road networks. This reduction occurs because vehicles must wait for the green phase of the traffic light to pass through the intersection. The reduction in traffic efficiency becomes more severe in the presence of emergency vehicles. Emergency vehicles always take priority over all other vehicles when proceeding through any signalized road intersection, even during the red phase of the traffic light. Inexperienced or careless drivers may cause an accident if they take inappropriate action during these scenarios. In this paper, we aim to design a dynamic and efficient traffic light scheduling algorithm that adjusts the best green phase time of each traffic flow, based on the real-time traffic distribution around the signalized road intersection. This proposed algorithm has also considered the presence of emergency vehicles, allowing them to pass through the signalized intersection as soon as possible. The phases of each traffic light are set to allow any emergency vehicle approaching the signalized intersection to pass smoothly. Furthermore, scenarios in which multiple emergency vehicles approach the signalized intersection have been investigated to select the most efficient and suitable schedule. Finally, an extensive set of experiments have been utilized to evaluate the performance of the proposed algorithm.     

Efficient image gradient based vehicle localization

This paper reports novel algorithms for the efficient localization and recognition of traffic in traffic scenes. The algorithms eliminate the need for explicit symbolic feature extraction and matching. The pose and class of an object is determined by a form of voting and one-dimensional (1-D) correlations based directly on image gradient data, which can be computed "on the fly." The algorithms are therefore very well suited to real-time implementation. The algorithms make use of two a priori sources of knowledge about the scene and the objects expected: (1) the ground-plane constraint and (2) the fact that the overall shape of road vehicles is strongly rectilinear. Additional efficiency is derived from making the weak perspective assumption. These assumptions are valid in the road traffic application domain. The algorithms are demonstrated and tested using routine outdoor traffic images. Success with a variety of vehicles in several traffic scenes demonstrates the efficiency and robustness of context-based image understanding in road traffic scene analysis. The limitations of the algorithms are also addressed.     

基于MSER和SVM的快速交通标志检测

为解决传统的基于机器学习的交通标志检测(TSD) 方法需要对每一个待检测子窗口进行处理而导致算法实时性不高的问 题,提出了一种基于感兴趣区域(ROI)提取和机器学习的快速TSD 算法。针对传统基于颜色阈值的ROI提取方法具 有对光照变化较敏感等缺点,设计了一种颜色增强下的最大稳定极值区域(MSER)方法 ,根据标志的颜色进行 颜色增强,对颜色增强图像提取MSER得到交通标志ROI;然后在图像的多尺度滑动遍历检测 过程中,仅对包含ROI的滑 动窗口进行方向梯度直方图(HOG)特征的提取,并通过支持向量机(SVM)进 行分类判别。实验结果表明,本文改进的TSD方法在运算速度上有较大提升,具有很好的鲁 棒性,且获得了96.42%的检测率以及较低的误检数。     

基于改进卷积神经网络的交通标志识别方法

针对智能交通系统中小尺度交通标志识别率低的问题,文中提出一种改进卷积神经网络的交通标志识别方法。该方法通过在Faster R-CNN算法的低层特征图上增加优化的RPN网络,提升了小尺度交通标志的检测率。该方法还利用Max Pooling方法实了现图像的局部细节特征与全局语义特征充分融合。在TT-100K数据集上稍微实验结果表明新方法可以明显提高小尺度交通标志的识别率。     

Nonlinear distortion-tolerant filters for detection of road signsin background noise

In a road sign recognition task, many distortions of targets can occur at the same time. Scale invariance, tolerance to both in-plane and out-of-plane rotations and illumination invariance are examples of features that a road sign recognition system must possess. We propose a nonlinear correlator that performs several correlations between an input scene and different reference targets. Postprocessing of nonlinear correlation results permits attainment of a single output for the recognition system. The nonlinear filters provide invariance to. distortions of the target, noise robustness, and rejection of background noise. We combine a bank of nonlinear composite correlation filters to design a more versatile road sign recognition system. The bank of filters allows tolerance to changes in scale and tolerance to a certain degree of input-plane rotation. The synthesized nonlinear composite correlation filter permits tolerance to out-of-plane rotation of the target. The system is tested by analysis of real images, which include different distorted versions of stop signs. The processor can be designed for a variety of road signs in background scenes. The recognition results obtained for the proposed system show its robustness against the aforementioned distortions, any varying illumination conditions and partially occluded objects     

Automatic Moving Vehicle Detection and Classification Based on Artificial Neural Fuzzy Inference System

The main objective of this work is to automatically detect moving vehicles on the road. Adaptive Neuro-Fuzzy Inference System (ANFIS) classifier is adopted in this paper to classify moving vehicles on road. An input traffic video scenes are taken with vertical and horizontal positioned cameras. The proposed system contains six major steps such as preprocessing, vehicle detection, tracking, structural matching, feature extraction and classification. In this proposed method, preprocessing consists of color conversion and noise removal. Vehicle detection is performed by using background subtraction and Otsu thresholding algorithm. Kalman filter is used in the third step to track moving vehicles in successive frames. In the fourth step, Active Shape Modelling method is used to recover the 3D shape of the vehicle in order to find the boundaries of vehicle. In the fifth step, features of the detected vehicles are extracted by Harrish corner detector, log Gabor filter and these features are taken into account to classify the types of vehicle. Finally, ANFIS is proposed to classify the vehicles which is trained by updating the membership function. Experimentation results provides better accuracy rate and low mean error rate when compared with the state of art methods.     

Inter-vehicle communication of warning information: an experimental study

In this paper, we show experimentally the feasibility of intervehicle communication of warning information. Warning messages convey significant information that might improve the safety of drivers and passengers. Intervehicle communication can be achieved by the detection of important events through a vision-based detection module, and sharing them between vehicles using a transmission module. In this paper, we developed a testbed that considers both modules in order to detect, recognize and share relevant information, such as traffic signs. To the best of our knowledge, our architecture is the first that combines detection and transmission of messages in the same platform. We detect traffic signs as blobs using the Maximally Stable Extremal Regions (MSER) algorithm, and we recognize them using Random forest classifiers. In the transmission module, we used a simplied broadcasting mechanism that avoids the use of handshaking to establish a communication. In order to assess our system, a set of indoor and outdoor experiments are considered.