A new approach has been developed for accurate and fast recognition of traffic signs based on human vision models. It applies colour appearance model CIECAM97 to segment traffic signs from the rest of scenes. This model takes viewing conditions into account and can recognise colours as accurate as an average observer invariant of lighting sources, background, and surrounding colours. A Behaviou...

In this paper we present a novel approach for the detection and recognition of traffic signs. Colour images are acquired b y a camera mounted in a car. In the first step these images are colour segmented with a pixel classifier. Colour combinations which are characteristic for trafic signs generate hypotheses. These hypotheses are verified using a pictogram classifier. Our system has been succe...

In this paper we present a fast and efficient match algorithm, which consists of two key techniques: Spectral Correlation Based Feature Merge(SCBFM) and Two-Step Retrieval(TSR). SCBFM can remove the redundant information. In consequence, the resulting feature sequence has a smaller size, requiring less storage and computation. In addition, most of the tempo variation is removed; thus a much sim...

We present a system for the large-scale automatic traffic signs recognition and mapping and experimentally justify design choices made for different components of the system. Our system works with more than 140 different classes of traffic signs and does not require labor-intensive labelling of a large amount of training data due to the training on synthetically generated images. We evaluated o...

This paper describes the COALA (Content-Oriented Audiovisual Library Access) project, which aims to offer a framework for the retrieval of audiovisual TV news documents. COALA’s principal design features are based on the analysis of a classical archive system: TSR (Television Suisse Romande) archive. It provides a retrieval interface allowing simple/advanced search capabilities combined with hi...

Paper presents basic information about application of Optical Correlator (OC), specifically Cambridge Correlator, in system to recognize of traffic sign. Traffic Sign Recognition System consists of three main blocks, Preprocessing, Optical Correlator and Traffic Sign Identification. The Region of Interest (ROI) is defined and chosen in preprocessing block and then goes to Optical Correlator, wh...

A system to learn and recognize traffic signs is described. The system uses neural network image processing and syntactic methods. The learning process is based on the representation of traffic signs by means of a grammar, which is inferred from a set of positive and negative samples. The recognition of traffic signs in a scene is done in two steps. First, the sign is located in the scene by us...

The real-time road traffic sign recognition focused on the application of computer vision techniques in automatic detection and classification of video images of traffic signs placed on roadways acquired from moving vehicles. In this work, we present results of an attempt to locating traffic signs placed on local roadways based on image processing techniques. The developed algorithm is based on...

0957-4174/$ see front matter 2012 Elsevier Ltd. A http://dx.doi.org/10.1016/j.eswa.2012.12.072 ⇑ Corresponding author. Tel.: +82 2 3408 3838. E-mail addresses: [email protected] (J.(K.-J. Kim). Traffic sign detection is a useful application for driving assistance systems, and it is necessary to accurately detect traffic signs before they can be identified. Sometimes, however, it is diff...

We briefly review the advances in driver assistance systems and present a realtime version that integrates single view detection with region-based 3D tracking of traffic signs. The system has a typical pipeline: detection and recognition of traffic signs in independent frames, followed by tracking for temporal integration. The detection process finds an optimal set of candidates and is accelera...