Road Sign Recognition Research Articles

Preprocessing of digital images in systems of location and recognition of road signs

The problem of localization and recognition of road signs is actual for today. Such a system can not only improve safety, compensating the probable human inattention, but it also helps to reduce tiredness, helping drivers keep an eye on the surrounding traffic conditions. This article proposes an efficient algorithm for preprocessing digital images for further detection of road signs in real time. The article considers the possibility of using HSV color space to extract the red. A denoising algorithm was developed to improve the accuracy and speed of detection. Parallel implementation on the GPU was used to remove the noise. The resulting images are best suited for further localization of road signs.

Image Segmentation using Learning Vector Quantization of Artificial Neural Network

This paper presents color image segmentation application for road-sign detection and recognition system based on Learning vector Quantization (LVQ). In traffic-sign maintenance and in a visual driver assistance system, road-sign detection and recognition are two of the most important functions. Our system is able to detect and recognize the signs. Road signs provide drivers important information and help them to drive more safely and more easily by guiding and warning them and thus regulating their actions. The RGB images are converted into HSV color space, and segmented using LVQ depending on the hue values of each pixel in the HSV color space. LVQ neural network is used to detect the color from the images and recognize them with high accuracy and speed. The detection and recognition of traffic signs represents an important issue in the field of traffic sign analysis. The paper researches the existing traffic sign detection and recognition methods. Special attention has been given to the methods based on artificial neural networks. Some techniques extracted various features from the images containing traffic signs and used various detectors and classifiers to perform detection and recognition. The following examples of these methods can be mentioned: Histogram of Oriented Gradient (2) or (1) and (6) or Support Vector Machines (SVM) (5). Some authors have also used artificial neural networks (4). This paper, however, uses a method of neural network (NN) which has given successful results. For individual object identification in humans, Color discrimination plays an important role. Image segmentation is the basic and first step regarding image analysis and pattern recognition. In image processing, image segmentation is not only the critical and essential component but also it is a very difficult task. The actual operation of the algorithm determines the accuracy and quality of image analysis. Color segmentation is a process of extracting one or more connected regions from the image domain. These connected regions are satisfying the uniformity (homogeneity) criterion which is derived from the spectral components. These components are then defined with respect to some color spaced model such as RGB model. RGB is the most common model. In RGB model, a color point is defined by the color components of the corresponding pixel which are defined in the form of Red (R), Green (G) and Blue (B). This paper considers a color image segmentation problem as a pixel classification problem. .

1G-11 Development of the Korean stroke drivers^|^apos; screening assessment: A Pilot Study for adapting road sign recognition subtest

1G-11 Development of the Korean stroke drivers^|^apos; screening assessment: A Pilot Study for adapting road sign recognition subtest

An Automatic Road Sign Recognizer for an Intelligent Transport System

This paper presents the implementation of an automatic road sign recognizer for an intelligent transport system. In this system, lists of road signs are processed with actions such as line segmentation, single sign segmentation, and storing an artificial sign in the database. The process of taking the video stream and extracting the road sign and storing in the database is called the road sign recognition. This paper presents a study on recognizing traffic sign patterns using a segmentation technique for the efficiency and the speed of the system. The image is converted from one scale to another scale such as RGB to grayscale or grayscale to binary. The images are pre-processed with several image processing techniques, such as threshold techniques, Gaussian filters, Canny edge detection, and the contour technique.

Design of a Multi-Sensor Cooperation Travel Environment Perception System for Autonomous Vehicle

This paper describes the environment perception system designed for intelligent vehicle SmartV-II, which won the 2010 Future Challenge. This system utilizes the cooperation of multiple lasers and cameras to realize several necessary functions of autonomous navigation: road curb detection, lane detection and traffic sign recognition. Multiple single scan lasers are integrated to detect the road curb based on Z-variance method. Vision based lane detection is realized by two scans method combining with image model. Haar-like feature based method is applied for traffic sign detection and SURF matching method is used for sign classification. The results of experiments validate the effectiveness of the proposed algorithms and the whole system.

Enhancement for road sign images and its performance evaluation

Recently, road sign recognition in natural scenes for intelligent transportation system (ITS) applications are paid much attention to, such as license plate recognition, traffic sign recognition and road text recognition. To improve recognition performance under some poor imaging conditions, e.g. complex lighting variations and pollution, an enhancement method for road sign images is proposed in this paper. It is composed of two steps: contrast maximizing while graying and mathematical morphological tophat–bothat transform. Performance evaluation results have demonstrated that the propose method can do well in purposiveness, real-time performance, and empirical parameter independence simultaneously, comparing to some other enhancement methods.

Recognition Stage for a Speed Supervisor Based on Road Sign Detection

Traffic accidents are still one of the main health problems in the World. A number of measures have been applied in order to reduce the number of injuries and fatalities in roads, i.e., implementation of Advanced Driver Assistance Systems (ADAS) based on image processing. In this paper, a real time speed supervisor based on road sign recognition that can work both in urban and non-urban environments is presented. The system is able to recognize 135 road signs, belonging to the danger, yield, prohibition obligation and indication types, and sends warning messages to the driver upon the combination of two pieces of information: the current speed of the car and the road sign symbol. The core of this paper is the comparison between the two main methods which have been traditionally used for detection and recognition of road signs: template matching (TM) and neural networks (NN). The advantages and disadvantages of the two approaches will be shown and commented. Additionally we will show how the use of well-known algorithms to avoid illumination issues reduces the amount of images needed to train a neural network.

Neural Network based Road Sign Recognition

A recent surge of interest is to recognize Road Signs. Signs are visual languages that represent some special circumstantial information of environment. They provide important information for guiding, warning people to make their movements safer, easier and more convenient. This thesis presents a hybrid neural network solution for Road sign recognition which combines local image sampling and artificial neural network. The method is based on BAM for dimensional reduction and multi-layer perception with backpropagation algorithm has been used for training the network. It has been found from practical observations that the number of iterations required to train the network is enormous. The capability of recognition of a neural network increases with increasing the training accuracy. For this process each sign is converted to a designated M×N feature matrix. These feature matrices of signs are then fed into the neural network as input patterns. The neural network is trained with the set of input patterns of the digits to acquire separate knowledge corresponding to each Road sign. In order to justify the effectiveness of the system, different test patterns of the signs are used to verify the system. Experimental results demonstrate that the system is capable of recognizing Road signs with 98% accuracy.

Road Sign Segmentation and Recognition under Bad Illumination Condition using Modified Fuzzy C-means Clustering

In this paper, we present a novel approach on road sign segmentation under bad lighting condition employing a modified fuzzy c-means clustering. Most of the cases accidents occur in bad weather or when the road signs cannot be recognized. The proposed system implements a system that segments the road sign using fuzzy c-means clustering. At first, the image is detected in RGB colour space and then converted into HSV colour model. After the conversion we detected the edge by applying Canny edge detector. Thus the proposed method is applied on the image. The method is based on the local similarity measure so that the noise created or the blurred image can easily be detected. But the traditional Fuzzy C-means clustering lack enough robustness to noise and preserving details of the image. Experimental results demonstrate that the system can segment the road signs successfully under bad illumination conditions. This system can be applied to typical type of traffic signs such as triangular, circle etc. The output result of the system is encouraging as the accuracy rate is 99%.

Detection and classification of road signs for automatic inventory systems using computer vision

This article describes an intelligent system that enables the automatic recognition of road signs from image sequences in road environments. The main difficulties the system has to deal with are related to changes in lighting conditions, obstacles bl

Introduction: Cognitive Dysfunction in Multiple Sclerosis

Introduction: Cognitive Dysfunction in Multiple Sclerosis

Road Sign Recognition with Fuzzy Adaptive Pre-Processing Models

A road sign recognition system based on adaptive image pre-processing models using two fuzzy inference schemes has been proposed. The first fuzzy inference scheme is to check the changes of the light illumination and rich red color of a frame image by the checking areas. The other is to check the variance of vehicle's speed and angle of steering wheel to select an adaptive size and position of the detection area. The Adaboost classifier was employed to detect the road sign candidates from an image and the support vector machine technique was employed to recognize the content of the road sign candidates. The prohibitory and warning road traffic signs are the processing targets in this research. The detection rate in the detection phase is 97.42%. In the recognition phase, the recognition rate is 93.04%. The total accuracy rate of the system is 92.47%. For video sequences, the best accuracy rate is 90.54%, and the average accuracy rate is 80.17%. The average computing time is 51.86 milliseconds per frame. The proposed system can not only overcome low illumination and rich red color around the road sign problems but also offer high detection rates and high computing performance.

Useful Field of View Predicts Driving in the Presence of Distracters

The Useful Field of View (UFOV) test has been shown to be highly effective in predicting crash risk among older adults. An important question which we examined in this study is whether this association is due to the ability of the UFOV to predict difficulties in attention-demanding driving situations that involve either visual or auditory distracters. Participants included 92 community-living adults (mean age 73.6 ± 5.4 years; range 65-88 years) who completed all three subtests of the UFOV involving assessment of visual processing speed (subtest 1), divided attention (subtest 2), and selective attention (subtest 3); driving safety risk was also classified using the UFOV scoring system. Driving performance was assessed separately on a closed-road circuit while driving under three conditions: no distracters, visual distracters, and auditory distracters. Driving outcome measures included road sign recognition, hazard detection, gap perception, time to complete the course, and performance on the distracter tasks. Those rated as safe on the UFOV (safety rating categories 1 and 2), as well as those responding faster than the recommended cut-off on the selective attention subtest (350 msec), performed significantly better in terms of overall driving performance and also experienced less interference from distracters. Of the three UFOV subtests, the selective attention subtest best predicted overall driving performance in the presence of distracters. Older adults who were rated as higher risk on the UFOV, particularly on the selective attention subtest, demonstrated poorest driving performance in the presence of distracters. This finding suggests that the selective attention subtest of the UFOV may be differentially more effective in predicting driving difficulties in situations of divided attention which are commonly associated with crashes.

Research on Road Signs Recognition Based on Machine Vision

The road traffic signs recognition in open environment is an important research in the field of safe auxiliary driving, vehicle autonomous navigation and so on. Based on the characteristic of road signs, one-dimensional entropy division was carried on to segment the AOI image obtained by lane detection, and then the road signs were recognized by SVM with improved Hu moment features. Experiments show that the proposed method for road traffic signs recognition in open environment has good reliability and robustness

A new approach in road sign recognition based on fast fractal coding

The tasks of traffic signs are notifying drivers about the current state of the road and giving them other important information for navigation. In this paper, a new approach for detection, tracking, and recognition such objects is presented. Road signs are detected using color thresholding, after that candidate blobs that have specific criteria are classified based on their geometrical shape and are tracked trough successive frames based on a new similarity measure. Candidate blobs that successfully pass the tracking module are processed for extracting their fractal features, and final recognition is done based on support vector machines with kernel function. Results validate effectiveness of newly employed fractal feature and show high accuracy with a low false hit rate of this method and its robustness to illumination changes and road sign occlusion or scale changes. Also results indicate that compared to the other pictogram feature representation techniques, this approach shows a more proper description of road signs.

Image Segmentation and Shape Analysis for Road-Sign Detection

This paper proposes an automatic road-sign recognition method based on image segmentation and joint transform correlation (JTC) with the integration of shape analysis. The presented system is universal, which is able to detect traffic signs of any countries with any color and any of the existing shapes (e.g., circular, rectangular, triangular, pentagonal, and octagonal) and is invariant to transformation (e.g., translation, rotation, scale, and occlusion). The main contributions of this paper are: 1) the formulation of two new criteria for analyzing different shapes using two basic geometric properties, 2) the recategorization of the rectangular signs into diamond or nondiamond shapes based on the inclination of the four sides with the ground and 3) the employment of the distortion-invariant fringe-adjusted JTC (FJTC) technique for recognition. There are three main stages in the proposed algorithm: 1) segmentation by clustering the pixels based on the color features to find the regions of interest (ROIs); 2) traffic-sign detection by using two novel shape classification criteria, i.e., the relationship between area and perimeter and the number of sides of a given shape; and 3) recognition of the road sign using FJTC to match the unknown signs with the known reference road signs stored in the database. Experimental results on real-life images show a high success rate and a very low false hit rate and demonstrate that the proposed framework is invariant to translation, rotation, scale, and partial occlusions.

Screening for fitness to drive after stroke

To identify the best determinants of fitness to drive after stroke, following a systematic review and meta-analysis. Twenty databases were searched, from inception until May 1, 2010. Potentially relevant studies were reviewed by 2 authors for eligibility. Methodologic quality was assessed by Newcastle-Ottawa scores. The fitness-to-drive outcome was a pass-fail decision following an on-road evaluation. Differences in off-road performance between the pass and fail groups were calculated using weighted mean effect sizes (d(w)). Statistical heterogeneity was determined with the I² statistic. Random-effects models were performed when the assumption of homogeneity was not met. Cutoff scores of accurate determinants were estimated via receiver operating characteristic analyses. Thirty studies were included in the systematic review and 27 in the meta-analysis. Out of 1,728 participants, 938 (54%) passed the on-road evaluation. The best determinants were Road Sign Recognition (d(w) 1.22; 95% confidence interval [CI] 1.01-1.44; I(2), 58%), Compass (d(w) 1.06; 95% CI 0.74-1.39; I², 36%), and Trail Making Test B (TMT B; d(w) 0.81; 95% CI 0.48-1.15; I(2), 49%). Cutoff values of 8.5 points for Road Sign Recognition, 25 points for Compass, and 90 seconds for TMT B were identified to classify unsafe drivers with accuracies of 84%, 85%, and 80%, respectively. Three out of 4 studies found no increased risk of accident involvement in persons cleared to resume driving after stroke. The Road Sign Recognition, Compass, and TMT B are clinically administrable office-based tests that can be used to identify persons with stroke at risk of failing an on-road assessment.

Traffic Sign Detection and Pattern Recognition Based on Binary Tree Support Vector Machines

This paper presents an automatic road sign detection and recognition system based on binary tree SVM. Color based segmentation techniques are employed for traffic sign detection. The coordinates position of traffic sign in images used for shape classification are obtained by orthogonal projection. An algorithm based on Hough transform was proposed to achieve better shape classification performance.Recognition of traffic signs are implemented using binary tree multi- classifer SVM with geometry semantic feature as the feature vector

Correlating Fourier descriptors of local patches for road sign recognition

The Fourier descriptors (FDs) is a classical but still popular method for contour matching. The key idea is to apply the Fourier transform to a periodic representation of the contour, which results in a shape descriptor in the frequency domain. FDs are most commonly used to compare object silhouettes and object contours; the authors instead use this well-established machinery to describe local regions to be used in an object-recognition framework. Many approaches to matching FDs are based on the magnitude of each FD component, thus ignoring the information contained in the phase. Keeping the phase information requires us to take into account the global rotation of the contour and shifting of the contour samples. The authors show that the sum-of-squared differences of FDs can be computed without explicitly de-rotating the contours. The authors compare correlation-based matching against affine-invariant Fourier descriptors (AFDs) and WARP-matched FDs and demonstrate that correlation-based approach outperforms AFDs and WARP on real data. As a practical application the authors demonstrate the proposed correlation-based matching on a road sign recognition task.

Robust Class Similarity Measure for Traffic Sign Recognition

Traffic sign recognition is an example of a hard multiclass classification problem. The existing approaches to that problem typically associate with each sign class a real-valued likelihood function and assign such a label to the unknown image that maximizes the value of this function. These template-matching techniques are usually based on arbitrary similarity metrics, such as normalized cross correlation, which do not capture the characteristics of the sign imagery. In this paper, we study the concept of a robust sign similarity measure that can be inferred from the domain-specific data. Two novel machine-learning techniques are proposed as a framework for automatic construction of such a measure from the pairs of images representing either the same or different classes. One is called SimBoost, which is a variation of the AdaBoost algorithm, and the other is based on the fuzzy regression tree framework. Through the experiments with low-quality images, we show that the proposed method admits efficient road sign recognition and outperforms the existing approaches in terms of the classification accuracy.