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

Fang, Chiung‐Yao; Fuh, Chiou-Shann; Yen, P.S.; Cherng, Shen; Chen, S. W.

doi:10.1016/j.cviu.2004.02.007

Cited by 104 publications

(44 citation statements)

References 30 publications

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“…A more recent work [9] presents an automatic road-sign detection and recognition system that is based on a computational model of human visual recognition processing. The system consists of three major components: 1) sensory; 2) perceptual; and 3) conceptual analyzers.…”

Section: State Of the Artmentioning

confidence: 99%

“…In [8], a nonlinear transformation over hue and saturation is employed to enhance the desired colors in the image (red and blue) using two lookup tables for every color for which we are looking. A similarity measurement between the hue component and the previously stored hue values of particular colors in road signs is calculated in [9], and this measurement is fed into a perceptual analyzer that is implemented by a neuronal network.…”

Section: State Of the Artmentioning

confidence: 99%

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Road-Sign Detection and Recognition Based on Support Vector Machines

Maldonado-Bascón

Lafuente-Arroyo

Gil-Jiménez

et al. 2007

IEEE Trans. Intell. Transport. Syst.

637

264

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Abstract-This paper presents an automatic road-sign detection and recognition system based on support vector machines (SVMs). In automatic traffic-sign maintenance and in a visual driverassistance system, road-sign detection and recognition are two of the most important functions. Our system is able to detect and recognize circular, rectangular, triangular, and octagonal signs and, hence, covers all existing Spanish traffic-sign shapes. 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 proposed recognition system is based on the generalization properties of SVMs. The system consists of three stages: 1) segmentation according to the color of the pixel; 2) traffic-sign detection by shape classification using linear SVMs; and 3) content recognition based on Gaussian-kernel SVMs. Because of the used segmentation stage by red, blue, yellow, white, or combinations of these colors, all traffic signs can be detected, and some of them can be detected by several colors. Results show a high success rate and a very low amount of false positives in the final recognition stage. From these results, we can conclude that the proposed algorithm is invariant to translation, rotation, scale, and, in many situations, even to partial occlusions.

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Section: State Of the Artmentioning

confidence: 99%

Section: State Of the Artmentioning

confidence: 99%

Road-Sign Detection and Recognition Based on Support Vector Machines

Maldonado-Bascón

Lafuente-Arroyo

Gil-Jiménez

et al. 2007

IEEE Trans. Intell. Transport. Syst.

637

264

View full text Add to dashboard Cite

show abstract

“…A total of 40 sign types were tested, 30 circular and 10 triangular, with a recognition rate reported of 94% for triangular signs and 91% for circular signs. The automatic road sign recognition system described in (Fang et al, 2004) reports very high classification results (99%), but the experiment was mostly centred on the detection of signs in video sequences. The recognition rate reported in (Kim et al, 2006) is also 99%, tested with 107 images, but only 10 sign types were considered.…”

Section: Introductionmentioning

confidence: 99%

Automatic Recognition of Road Signs in Digital Images for Gis Update

Marçal

Gonçalves²

2009

Proceedings of the First International Conference on Computer Imaging Theory and Applications

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Abstract:A method for automatic recognition of road signs identified in digital video images is proposed. The method is based on features extracted from cumulative histograms and supervised classification. The training of the classifier is done with a small number of images (1 to 6) from each sign type. A practical experiment with 260 images and 26 different road sign was carried out. The average classification accuracy of the method with the standard settings was found to be 93.6%. The classification accuracy is improved to 96.2% by accepting the sign types ranked 1 st and 2 nd by the classifier, and to 97.4% by also accepting the sign type ranked 3 rd . These results indicate that this can be a valuable tool to assist Geographic Information System (GIS) updating process based on Mobile Mapping System (MMS) data.

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“…Fang, Chiung-Yao presents an automatic road sign detection and recognition system that is based on a computational model of human visual recognition processing [6]. Lorsakul, Auranuch, and Jackrit Suthakorn presents a study to recognize traffic sign patterns using Neural Networks technique [14].In paper [18], a low-power real-time traffic sign recognition system that is robust under various illumination conditions is proposed.…”

Section: Introductionmentioning

confidence: 99%

Real Time Traffic Sign Detection and Recognition using Adaptive Neuro Fuzzy Inference System

Billah¹,

Waheed²,

Ahmed³

et al. 2015

CAE

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Traffic sign recognition is a major part of an automated intelligent driving vehicle or driver assistance systems.Perfect recognition of traffic sign helps an intelligent driving system giving valuable information about road signs,warnings, prohibitions thus increasing driving speed, security and decreasing risk of accident. Many techniques have been used for recognising traffic signs such as backpropagation neural network,support vector machines,convolutional neural network etc on different shaped signs. Fuzzy inference system has not been used in deep for this purpose. In this paper, we have tried to find out the capability of adaptive neuro fuzzy inference system(ANFIS) for traffic sign recognition. We have used video and image processing for detecting circular shaped signs and used ANFIS for recognizing detected signs.

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

Cited by 104 publications

References 30 publications

Road-Sign Detection and Recognition Based on Support Vector Machines

Road-Sign Detection and Recognition Based on Support Vector Machines

Automatic Recognition of Road Signs in Digital Images for Gis Update

Real Time Traffic Sign Detection and Recognition using Adaptive Neuro Fuzzy Inference System

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