Fast and robust road sign detection in driver assistance systems

© 2018, Springer Science+Business Media, LLC, part of Springer Nature. Road sign detection plays a critical role in automatic driver assistance systems. Road signs possess a number of unique visual qualities in images due to their specific colors and symmetric shapes. In this paper, road signs are detected by a two-level hierarchical framework that considers both color and shape of the signs. To address the problem of low image contrast, we propose a new color visual saliency segmentation algorithm, which uses the ratios of enhanced and normalized color values to capture color information. To improve computation efficiency and reduce false alarm rate, we modify the fast radial symmetry transform (RST) algorithm, and propose to use an edge pairwise voting scheme to group feature points based on their underlying symmetry in the candidate regions. Experimental results on several benchmarking datasets demonstrate the superiority of our method over the state-of-the-arts on both efficiency and robustness

Automating the Process of Traffic Orientation Through Mobile Devices and Ontologies

Mobile devices are used in all activities undertaken by users and 90% of them have used at least once a mobile device to search for local information navigation and acted on the basis of data. In this material is presented the use of mobile applications for traffic navigation or assistance and how they can contribute to the automation of orientation in traffic through traffic signs. Traffic signs around the globe are very different, even if some countries ratified conventions or adopted common specifications. In addition to that, a part of traffic signs differ from country to country even if they have the same road signal convention. This paper work aims to establish a global knowledge base with traffic signs and traffic rules dictated by them. In this way when a driver travels in foreign countries by car he can be helped by the mobile device in order to recognize the traffic signs. The ontology design is made by using Protégé software together with an RDF/RDFS approach. It uses a class hierarchy with classes like RoadSign and TrafficRule in the top of it. SPAQRL is the query language used to clean the knowledge base. At the beginning it will be populated with traffic signs from Romania. Ontology will be the backend of the mobile application that provides recognition of traffic signs and assists drivers from around the world in traffic navigation. In order to motivate the users to be active in the community and add new signs in the application a gamification approach is used

Vision-Based Traffic Sign Detection and Recognition Systems: Current Trends and Challenges

The automatic traffic sign detection and recognition (TSDR) system is very important research in the development of advanced driver assistance systems (ADAS). Investigations on vision-based TSDR have received substantial interest in the research community, which is mainly motivated by three factors, which are detection, tracking and classification. During the last decade, a substantial number of techniques have been reported for TSDR. This paper provides a comprehensive survey on traffic sign detection, tracking and classification. The details of algorithms, methods and their specifications on detection, tracking and classification are investigated and summarized in the tables along with the corresponding key references. A comparative study on each section has been provided to evaluate the TSDR data, performance metrics and their availability. Current issues and challenges of the existing technologies are illustrated with brief suggestions and a discussion on the progress of driver assistance system research in the future. This review will hopefully lead to increasing efforts towards the development of future vision-based TSDR system. Document type: Articl

Determination of Driver Needs in Work Zones

DOT-FH-11-9549The objectives of the study were: (a) to determine what information drivers need to travel through work zones safely and efficiently; (b) to determine how this information can best be conveyed to the drivers; and (c) to determine where improvements to the present system of work zone traffic control are needed. The study began with an analysis of driver tasks for eight major work zone types: lane closure, shoulder closure, roadside, lane diversion, crossover, temporary detour, detour to alternate routes, and reduced lane width. From this effort, a set of information content needs was identified for each work zone type. A further analytic effort using the principles of the Positive Guidance Procedure and the concept of Decision Sight Distance, resulted in the identification of recommended information presentation locations for the various types of information. These analytic efforts were combined into information requirements which were then evaluated for applicability by exercising each on a series of actual work zones. The requirements were modified where necessary and were then used as the basis for the development of a procedure for the derivation of work zones signing plans. Another aspect of the project involved the evaluation of individual construction-related signs, in which each device was evaluated with respect to several criteria and problems were identified

Unconstrained Road Sign Recognition

There are many types of road signs, each of which carries a different meaning and function: some signs regulate traffic, others indicate the state of the road or guide and warn drivers and pedestrians. Existent image-based road sign recognition systems work well under ideal conditions, but experience problems when the lighting conditions are poor or the signs are partially occluded. The aim of this research is to propose techniques to recognize road signs in a real outdoor environment, especially to deal with poor lighting and partially occluded road signs. To achieve this, hybrid segmentation and classification algorithms are proposed. In the first part of the thesis, we propose a hybrid dynamic threshold colour segmentation algorithm based on histogram analysis. A dynamic threshold is very important in road sign segmentation, since road sign colours may change throughout the day due to environmental conditions. In the second part, we propose a geometrical shape symmetry detection and reconstruction algorithm to detect and reconstruct the shape of the sign when it is partially occluded. This algorithm is robust to scale changes and rotations. The last part of this thesis deals with feature extraction and classification. We propose a hybrid feature vector based on histograms of oriented gradients, local binary patterns, and the scale-invariant feature transform. This vector is fed into a classifier that combines a Support Vector Machine (SVM) using a Random Forest and a hybrid SVM k-Nearest Neighbours (kNN) classifier. The overall method proposed in this thesis shows a high accuracy rate of 99.4% in ideal conditions, 98.6% in noisy and fading conditions, 98.4% in poor lighting conditions, and 92.5% for partially occluded road signs on the GRAMUAH traffic signs dataset

Traffic and road sign recognition

This thesis presents a system to recognise and classify road and traffic signs for the purpose of developing an inventory of them which could assist the highway engineers' tasks of updating and maintaining them. It uses images taken by a camera from a moving vehicle. The system is based on three major stages: colour segmentation, recognition, and classification.Four colour segmentation algorithms are developed and tested. They are a shadow and highlight invariant, a dynamic threshold, a modification of de la Escalera's algorithm and a Fuzzy colour segmentation algorithm. All algorithms are tested using hundreds of images and the shadow-highlight invariant algorithm is eventually chosen as the best performer. This is because it is immune to shadows and highlights. It is also robust as it was tested in different lighting conditions, weather conditions, and times of the day.Approximately 97% successful segmentation rate was achieved using this algorithm. Recognition of traffic signs is carried out using a fuzzy shape recogniser. Based on four shape measures - the rectangularity, triangularity, ellipticity, and octagonality, fuzzy rules were developed to determine the shape of the sign. Among these shape measures octangonality has been introduced in this research. The final decision of the recogniser is based on the combination of both the colour and shape of the sign. The recogniser was tested in a variety of testing conditions giving an overall performance of approximately 88%.Classification was undertaken using a Support Vector Machine (SVM) classifier. The classification is carried out in two stages: rim's shape classification followed by the classification of interior of the sign. The classifier was trained and tested using binary images in addition to five different types of moments which are Geometric moments, Zernike moments, Legendre moments, Orthogonal Fourier-Mellin Moments, and Binary Haar features. The performance of the SVM was tested using different features, kernels, SVM types, SVM parameters, and moment's orders. The average classification rate achieved is about 97%. Binary images show the best testing results followed by Legendre moments. Linear kernel gives the best testing results followed by RBF. C-SVM shows very good performance, but v-SVM gives better results in some case