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The proper identification of speed limit traffic sighs can alarm the drivers the highest speed allowed and can effictively reduce the number of traffic accidents. In this paper, we put forward an efficient detection method for speed limit traffic signs based on the fast radial symmetry transform with new Sobel operator. when we detected the speed limit traffic sign, we need to segment the digits. Digit segmentation is achieved by cropping the candidate traffic sign from the traffic scene, making use of Otsu thresholding algorithm to binary it, and normalizing it to a uniform size. Finally we recognize and classify the signs using DAG-SVMs classifier which is trained for this purpose. In cloudy weather conditions and dusk illumination condition, we tested 10 videos about 28 min. The recognition rate of frames which contain speed limit sign is 90.48 %.

This paper aims to deal with real-time traffic sign detection. To this end, a two-stage method is proposed to reduce the processing time with little influence to AUC (area under curve) value. In first stage, a color probability model is proposed to transform an input image to probability maps. The traffic sign proposals are then extracted by finding maximally stable extremal regions on these maps. In second stage, an integral channel features detector is employed to remove false positives of the proposals. Experiments on the GTSDB benchmark [1] show that the proposed color probability model achieves the highest recall rate and the proposed two-stage method significantly improves computational efficiency with good AUC value in comparison with the state-of-the-art methods.

The recognition of traffic signs in natural environment is a challenging task in computer vision because of the influence of weather conditions, illuminations, locations, vandalism, and other factors. In this paper, we propose a vision-based traffic sign recognition system for the real utilization of intelligent vehicles. The proposed system consists of two phases: detection and recognition. In detection phase, we employ simple vector filter for chromatic/achromatic discrimination and color segmentation followed by shape analysis to roughly divide traffic signs into seven categories according to the color and shape properties. The Pseudo-Zernike moments features of the extracted candidate traffic sign regions are selected for recognition by random forests which combines bootstrap aggregating (bagging) algorithm and random feature selection to construct collections of decision trees and possesses excellent classification ability. The rationality and effectiveness of the proposed system is validated on our intelligent vehicle—Intelligent Pioneer from a great number of experiments.

Mining discriminative spatial patterns in image data is a subject of interest in traffic sign recognition. In this paper, we use an approach for detecting spatial regions that are highly discriminative. The main idea is to search the normalized size blobs for discriminative regions by adaptively partitioning the space into progressively smaller sub-regions. Thus, each cluster of signs is characterized by an unique region pattern which consists of homogeneous and discriminant 2-D regions. The mean intensities of these regions are used as features. To evaluate the discriminative power of the attributes corresponding to detected regions, we performed classification experiments using a classifier based on Support Vector Machines. The proposed method has been tested in a real traffic sign database. Results demonstrate that the method can achieve a considerable reduction of features with respect to extraction from raw images while maintaining accurate.

Smart Cities is a vision driven by the availability of governmental data that fosters many challenging applications. One of them is the management of inconsistent traffic regulations, i.e., the handling of inconsistent traffic signs and measures in urban areas such as wrong sign posting, or errors in data acquisition in traffic sign administration software. We investigate such inconsistent traffic scenarios and formally model traffic regulations using a logic-based approach for traffic signs and measures, and logical theories describe emerging conflicts on a graph-based street model. Founded on this model, we consider major reasoning tasks including consistency testing, diagnosis, and repair, and we analyze their computational complexity for different logical representation formalisms. Our results provide a basis for an ongoing implementation of the approach.

We construct a driving environment reconstruction and analysis system based on multi-sensors network onboard and some functional subsystem as well. With the data acquired, processed and stored, the real comprehensive driving environment, which includes vehicle dynamic state information, traffic environment information and driving behavior information, can be established accurately and provide what had happened in and around the vehicle. Besides, this system can also provide the researchers with additional and important information, for example traffic sign, moving object and driver gaze information. Practical results show this system is a very powerful technical framework to deep incident analysis and a quantitative evaluation measure to the effect of passive and active safety technologies, which can promote and formulate vehicle safety measures or reduce serious injuries and disabilities in addition to the reduction of fatalities and injuries in general.

The recognition of traffic signs in many state-of-the-art driver assistance systems is performed by statistical pattern classification methods. Traffic signs in European countries share many similarities but also vary in colour, size, and depicted symbols, making it hard to obtain one general classifier with good performance in all countries. Training separate classifiers for each country requires huge amounts of labelled training data. A well-trained classifier for one country can be adapted to other countries by semi-supervised learning methods to perform reasonably well with relatively low requirements regarding labelled training data. Self-training classifiers adapting themselves to unknown domains always risk that the adaption will become ineffective or even fail completely due to the occurrence of incorrectly labelled samples. To assure that self-training classifiers adapt themself correctly, advanced multi-classifier training methods like co-training are applied.

This paper presents the design and application of novel device for real time traffic sign detection and recognition on a hardware platform powered by Intel® AtomTM processor. Image frames from standard and relatively cheap web cameras are processed using OpenCV library [7][2]. An innovative method is proposed for traffic sign detection phase. Two color models are used for image segmentation and detection of traffic sign. Many well-known and described tactics have been tested and rated. Implemented in OpenCV Library functions for pattern recognition method are also used in main algorithm. Experimental results of traffic sign detection and recognition are described. The prototype was implemented as part of the Master Thesis at Cracow University of Technology [1].

This paper proposes a method for constructing an accurate traffic sign detector by retrospectively obtaining training samples from in-vehicle camera image sequences. To detect distant traffic signs from in-vehicle camera images, training samples of distant traffic signs are needed. However, since their sizes are too small, it is difficult to obtain them either automatically or manually. When driving a vehicle in a real environment, the distance between a traffic sign and the vehicle shortens gradually, and proportionally, the size of the traffic sign becomes larger. A large traffic sign is comparatively easy to detect automatically. Therefore, the proposed method automatically detects a large traffic sign, and then small traffic signs (distant traffic signs) are obtained by retrospectively tracking it back in the image sequence. By also using the retrospectively obtained traffic sign images as training samples, the proposed method constructs an accurate traffic sign detector automatically. From experiments using in-vehicle camera images, we confirmed that the proposed method could construct an accurate traffic sign detector.

This chapter shows the results of a terminological and linguistic approach to analyse traffic/road signs and verbal messages and their conceptual elements, with a view to achieving more consistency within and among countries. Work in this chapter started off from defining key meanings and bilingual messages for VMSs. It covers quite a range of different aspects from the optimization of some of the existing road/traffic signs (and their verbal and non-verbal elements) and VMSs (variable message signs) via the information design of the data contained therein, to Europe-wide harmonization and testing. It is, therefore, complementary to the previous Chap. 13, which focuses more on the layout and pictograms. In the course of IN-SAFETY, it became clear that some road/traffic signs, as well as additional signs and VMSs or (verbal or non-verbal) elements thereof should – and could easily – be harmonized, not only for the benefit of pan-EU traffic information/control systems, but especially as regards user-friendliness to support SER and FOR – thus diminishing the potential of accidents caused by distracted drivers. In order to obtain these results, a systematic methodology was developed for the aspects under investigation and applied as “Europeanisms”, encompassing key meanings and potential objects for harmonization.

The paper represents a vision-based lane departure warning system, as well as, a driver assistance system for the automatic recognition of traffic signs. It could inform the driver about actual speed limits or passing restrictions, which could be ignored in difficult traffic situations. The system is implemented partially on a new multi-core processor, developed by the Infineon Technologies AG.

In this paper a system is presented for real time recognition of the traffic signs. Sign detection is done by a method of adaptively growing window. Classification is based on matching of the modulo-shifted phase histograms. These are built from the stick component of the structural tensor rather than from an edge detector. To cope with inherent rotations of signs a novel measure is proposed for matching of the modulo-shifted histograms that also boosts responses of highly probable values. The method is tolerant of small translations, rotations and symmetrical changes of scale. It works also well under different lighting conditions and tolerates noise and small occlusions.

Recognition of traffic signs by systems of the intelligent vehicles can increase safety and comfort of driving. It can be also used for highway inspection. In this paper we present architecture of such a system, with special focus on fast sign tracking method. In each frame an adaptive window is built around each area with high probability of existence of an object to be tracked. However, contrary to the mean-shift algorithm, it is not necessary to compute a centroid for each such object. Thus the method allows faster execution which is a key parameter for the real-time scene analysis.

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.

In traffic sign recognition systems, one of the normal approaches is the identification of the shape of the sign prior to the recognition itself. Normally, the recognition process needs an accurate localization of the sign for a good performance. If we are dealing with triangular, rectangular and circular signs, this means the accurate localization of the vertices of the triangle and the rectangle, or the parameters of the ellipse. In this paper we have developed a system which searches the above mentioned parameters from the signature of the blob using techniques of algebraic-distance minimization. Comparisons with previous works show good improvements in the localization of the shape, especially in the presence of slight occlusions. This work is part of a traffic sign recognition system, and in this paper we focus on the shape localization step.

A fast method for the recognition and classification of informational traffic signs is presented in this paper. The aim is to provide an efficient framework which could be easily used in inventory and guidance systems. The process consists of several steps which include image segmentation, sign detection and reorientation, and finally traffic sign recognition. In a first stage, a static HSI colour segmentation is performed so that possible traffic signs can be easily isolated from the rest of the scene; secondly, shape classification is carried out so as to detect square blobs from the segmented image; next, each object is reoriented through the use of a homography transformation matrix and its potential axial deformation is corrected. Finally a recursive adaptive segmentation and a SVM-based recognition framework allow us to extract each possible pictogram, icon or symbol and classify the type of the traffic sign via a voting-scheme.

In the adaptive cruise control system, the scanning laser radar is widely used to detect the preceding car even on the winding road. The conventional scanning laser radar scans only horizontally, utilizing a fan beam which is horizontally narrow and vertically wide. Since the conventional system scans only horizontally, it has the following drawback: To detect the preceding car even on the highway with up/down hill, it is necessary to utilize a vertically wide fan beam. Therefore, while the car is traveling on a flat road, the scanning laser radar may detect the objects above the road, such as traffic signs, guideboards and bridges etc., in stead of the preceding cars.