An automatic road sign recognition system based on a computational model of human recognition processing

This paper explores the effective approach of road sign detection and recognition for Driver Assistance Systems (DAS). In today's world road conditions drastically improved as compared with past decade. Express highways equipped with increased lane size made up with cement concrete. Obviously speed of the vehicle increased. So on driver point of view there might be chances of neglecting mandatory road sign while driving. This paper illustrates proposed system to help driver about the road sign detection to avoid road accidents. The automatic road-signs recognition is an important part of Driver Assisting Systems which helps driver to increase safety and driving comfort. In this paper an efficient approach for the detection and recognition of the road sign in the road and acquiring the traffic scene images from a moving vehicle is present. In this paper the road sign recognition system is to be divided into two parts, the first part is detection stage which is used to detect the signs from a whole image, and the second part is classification stage that classifies the detected sign in the first part into one of the reference signs which are presents in the dataset. In the detection module segments, the input image in a YCBCR colour space, and then it detects road signs by using the shape filtering method. The classification module present determines the type of detected road signs by using an artificial neural network (ANN). The extensive experimentation has shown that the proposed system approach is robust enough to detect and the recognize road signs under varying lighting, rotation and translation conditions.

The increase in traffic accidents is becoming a serious social problem with the recent rapid traffic increase. In many cases, the driver’s carelessness is the primary factor of traffic accidents, and the driver assistance system is demanded for supporting driver’s safety. In this research, we propose the new method of automatic detection and recognition of road signs by image processing. The purpose of this research is to prevent accidents caused by driver’s carelessness, and call attention to a driver when the driver violates traffic a regulation. In this research, high accuracy and the efficient sign detecting method are realized by removing unnecessary information except for a road sign from an image, and detect a road sign using shape features. At first, the color information that is not used in road signs is removed from an image. Next, edges except for circular and triangle ones are removed to choose sign shape. In the recognition process, normalized cross correlation operation is carried out to the two-dimensional differentiation pattern of a sign, and the accurate and efficient method for detecting the road sign is realized. Moreover, the real-time operation in a software base was realized by holding down calculation cost, maintaining highly precise sign detection and recognition. Specifically, it becomes specifically possible to process by 0.1 sec(s)/frame using a general-purpose PC (CPU: Pentium4 1.7GHz). As a result of in-vehicle experimentation, our system could process on real time and has confirmed that detection and recognition of a sign could be performed correctly.

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 road signs assisting the driver of the vehicle to properly operate the vehicle. This paper presents an automatic road sign recognition system capable of analysing live images, detecting multiple road signs within images, and classifying the type of the detected road signs. The system consists of two modules: detection and classification. The detection module segments the input image in the hue-saturation-intensity colour space and locates road signs. The classification module determines the type of detected road signs using a series of one to one architectural Multi Layer Perceptron neural networks. The performances of the classifiers that are trained using Resillient Backpropagation and Scaled Conjugate Gradient algorithms are compared. The experimental results demonstrate that the system is capable of achieving an average recognition hit-rate of 96% using Scaled Conjugate Gradient trained classifiers.

Road safety is influenced by the adequate placement of traffic signs. As the visibility of road signs degrades over time due to e.g. aging, vandalism or vegetation coverage, sign maintenance is required to preserve a high road safety. This is commonly performed based on inventories of traffic signs, which should be conducted periodically, as road situations may change and the visibility of signs degrades over time. These inventories are created efficiently from street-level images by (semi-)automatic road sign recognition systems, employing computer vision techniques for sign detection and classification. Instead of periodically repeating the complete surveying process, these automated sign recognition systems enable re-identification of the previously found signs. This results in the highlighting of changed situations, enabling specific manual validation of these cases. This paper presents a mutation detection approach for semi-automatic updating of traffic sign inventories, together with a case study to assess the practical usability of such an approach. Our system re-identifies 94.8% of the unchanged signs, thereby resulting in a significant reduction of the manual effort required for the semi-automated actualization of the inventory. As the amount of changes equals to 16.9% of the already existing signs, this study also clearly shows the economic relevance and usefulness of periodic updating road sign surveys.

Traffic Sign Recognition (TSR) system is a vital component of intelligent transport system. It plays an important role by enhancing the safety of the drivers, pedestrians and vehicles as traffic signs provide important information of the traffic environment of the road and assist the drivers to drive more safely and easily by guiding and warning. This paper represents road sign detection and recognition system based on speeded up robust feature (SURF) descriptor that is invariant to rotation, skew and occlusion of the sign and artificial neural network (ANN) classifier. The proposed system consists of three steps. In the first step the road scene image is segmented using color based thresholding algorithm followed by some post processing like morphological analysis and filtering using region properties to extract the road sign as a blob of region. Next, shape verification is carried out by using translation, scaling and rotation invariant features vector named Distance to Borders (DtBs) of the segmented blob. Finally the recognition of the traffic sign is implemented using ANN classifier with the training of SURF descriptor. The proposed system simulated on offline road scene images shows a high success rate in the recognition stage. The performance of the classifier is illustrated in terms of cross entropy, confusion matrix and receiver operating characteristic (ROC) curve.

Automatic detection and recognition of road traffic signs is an important work for regulating the traffic and guiding and warning drivers and pedestrians. The main aim of this work is to compare the performance of the road sign detection, extraction and recognition techniques and find the best one. This project investigates two techniques. In the first approach, Color Classification is used for detecting the road sign. After extraction, Wavelet based classification is used for recognize the road sign. In the second approach, Color Segmentation and Shape Classification is used for detect the road sign. After detecting the road sign Local energy based shape histogram (LESH) is used for recognize the road sign. To evaluate the performance of the above two approaches Recognition Rate (RR) metric is used. From the evaluation, we conclude that the performance of LESH method provides better performance from the overall results.

The paper presents a system for the detection and recognition of road signs in real time (tested for Indian road signs). The detection and recognition algorithm used is invariant to scale, angle, blur extent, and variation in lighting condition. Shape classification of road signs using Hu moments is done in order to categorize signs as either warning, mandatory, prohibitory or informational. Classified road signs are then matched to ideal road signs using feature extraction, and the matching is done with the help of Oriented FAST and Rotated BRIEF (ORB) descriptors. After recognition, the driver is given a feedback.

Automatic road sign recognition systems require a great computational cost since the number of different signs in each country is quite large. Inmany real-world applications only a reduced subset of traffic signs is considered in the recognition stage to verify the success of a classification algorithm. This paper proposes a optimization in the traffic sign identification task working in the spatial domain. This purpose is overcome through dimension reduction methods, such as 2DPCA and (2D)2PCA, to perform principal component analysis of training and test image vectors. The applications of these advances, using SVMs as classification technique, are shown here over a considerable database.

Using self-organising maps in the detection and recognition of road signs

This paper describes an efficient approach towards road sign detection and recognition. The proposed system is divided into three sections namely; Colour Segmentation of the road traffic signs using the HSV colour space considering varying lighting conditions, Shape Classification using the Contourlet Transform considering occlusion and rotation of the candidate signs and the Recognition of the road traffic signs using features of a Local Energy based Shape Histogram (LESH). We have provided three experimental results and a detailed analysis to justify that the algorithm described in this paper is robust enough to detect and recognize road signs under varying weather, occlusion, rotation and scaling conditions using video stream.

In this paper, we propose and validate a new system used to explore road assets. In this work we are interested on the vertical road signs. To do this, we are based on the combination of road signs detection, recognition and identification using data provides by sensors. The proposed approach consists on using panoramic views provided by the innovative device, VIAPIX®¹, developed by our company ACTRIS². We are based also on the optimized correlation technique for road signs recognition and identification on pictures. Obtained results shows the interest on using panoramic views compared to results obtained using images provided using only one camera.

Automobile industries have developed many features for providing safety to the vehicles. Among these, a feature known as road sign detection system is under the research topic in Automobile safety. Thus, this paper has proposed a feature which will be present in a vehicle and will help the driver to detect the road sign through image processing. It includes the algorithm regarding the detection of a road sign by using three stage detection method which are color, shape and sign respectively. The method used has been applied to a set 100 images in which the results have also been discussed and it was found that around 98% cases the proposed system is found to be effective. It has also considered the various exceptions which occurred during the implementation.

The automatic detection and recognition of road signs play important role in the driver assistance systems and can increase the safety on the roads. In this paper we propose a system of a road signs classifier which is based on ensemble of the non Euclidean distance neural networks and an arbitration unit. The input to this system comes from the sign detection module which supplies a normalized, binarized and resampled pictogram of a detected sign. The system performs classification on deformable models. The classifier is composed of a mixture of experts (binary distance neural networks) operating on slightly tilted or shifted versions of pictograms. This ensemble of experts is orchestrated by an arbitration module which operates in the winner-takes-all mode with a novel modification of promoting the most populated group of unanimous experts. The experimental results showed great robustness of the system and very fast response time which is an important factor in the driving assistance systems.

This paper addresses an important issue — automatic road traffic signs detection and recognition in natural scenes, which is accomplished in the three following stages: interest points detection, clustering of those points and similarity search. At the first stage, good discriminative, rotation and scale invariant interest points are selected from the image edges based on the 1-D empirical mode decomposition (EMD). Then stable local features related to the brightness and color are extracted using Gabor filter and the detected points are clustered to find the possible candidate road signs or the region of interests (ROIs). We propose a two-step unsupervised clustering technique, which is adaptive. Finally, a fringe-adjusted joint transform correlation (JTC) technique is used for matching the unknown signs with the existing known reference road signs in the database. The presented framework provides a novel way to retrieve a road sign from the natural scenes.

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.