Traffic sign detection based on AdaBoost color segmentation and SVM classification

Fleyeh, Hasan; Biswas, Rubel; Davami, Erfan

doi:10.1109/eurocon.2013.6625255

Cited by 18 publications

(16 citation statements)

References 10 publications

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“…Based on the k -means clustering algorithm, the authors build a classifier that employs the

a^{*}

and

b^{*}

components. Fleyeh et al [23] use the H and S components of the HSV space to train a classifier and implement the color segmentation that yields ROIs. More recently, Han et al [24] have used the H component of the

H S I

space, in which the traffic signs are highlighted in order to build a grayscale image where a set of ROIs is generated.…”

Section: State-of-the-artmentioning

confidence: 99%

Traffic Sign Detection System for Locating Road Intersections and Roundabouts: The Chilean Case

Villalón-Sepúlveda

Torres-Torriti

Flores-Calero

2017

Sensors

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This paper presents a traffic sign detection method for signs close to road intersections and roundabouts, such as stop and yield (give way) signs. The proposed method relies on statistical templates built using color information for both segmentation and classification. The segmentation method uses the RGB-normalized (ErEgEb) color space for ROIs (Regions of Interest) generation based on a chromaticity filter, where templates at 10 scales are applied to the entire image. Templates consider the mean and standard deviation of normalized color of the traffic signs to build thresholding intervals where the expected color should lie for a given sign. The classification stage employs the information of the statistical templates over YCbCr and ErEgEb color spaces, for which the background has been previously removed by using a probability function that models the probability that the pixel corresponds to a sign given its chromaticity values. This work includes an analysis of the detection rate as a function of the distance between the vehicle and the sign. Such information is useful to validate the robustness of the approach and is often not included in the existing literature. The detection rates, as a function of distance, are compared to those of the well-known Viola–Jones method. The results show that for distances less than 48 m, the proposed method achieves a detection rate of 87.5% and 95.4% for yield and stop signs, respectively. For distances less than 30 m, the detection rate is 100% for both signs. The Viola–Jones approach has detection rates below 20% for distances between 30 and 48 m, and barely improves in the 20–30 m range with detection rates of up to 60%. Thus, the proposed method provides a robust alternative for intersection detection that relies on statistical color-based templates instead of shape information. The experiments employed videos of traffic signs taken in several streets of Santiago, Chile, using a research platform implemented at the Robotics and Automation Laboratory of PUC to develop driver assistance systems.

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“…Based on the k -means clustering algorithm, the authors build a classifier that employs the

a^{*}

and

b^{*}

H S I

space, in which the traffic signs are highlighted in order to build a grayscale image where a set of ROIs is generated.…”

Section: State-of-the-artmentioning

confidence: 99%

Traffic Sign Detection System for Locating Road Intersections and Roundabouts: The Chilean Case

Villalón-Sepúlveda

Torres-Torriti

Flores-Calero

2017

Sensors

View full text Add to dashboard Cite

show abstract

“…Para la detección de señales de tráfico se suele seguir una estrategia compuesta de dos partes, la segmentación (también conocida como detección) y el reconocimiento (Greenhalgh and Mirmehdi, 2012;Salti et al, 2015;Li et al, 2015;Stanciulescu, 2012, 2014;Mogelmose et al, 2012;Carrasco, 2009;Fleyeh et al, 2013;Han et al, 2015).…”

Section: Estado Del Arteunclassified

“…Con el algoritmo k − means (Hastie et al, 2009) han construido un clasificador usando las componentes a * y b * . Fleyeh et al (Fleyeh et al, 2013) han usado las componentes H y S del espacio HS V para entrenar el clasificador AdaBoost y construir un método de segmentación por color para la generación de ROIs. Finalmente, Han et al (Han et al, 2015) han usado la componente H del espacio HS I, donde han generado un intervalo, donde resaltan las señales de tráfico, para construir una imagen en grises donde se localizan las ROIs.…”

Section: Segmentaciónunclassified

“…Lillo et al (Lillo et al, 2015) han implementado la extracción de caractarísticas usando la transformada discreta de Fourier. Fleyeh et al (Fleyeh et al, 2013) han implementado su método con la transformada de Hough. Han et al (Han et al, 2015) utilizaron el método S URF (Baya et al, 2008).…”

Section: Reconocimientounclassified

“…En las señales de tráfico predominan los colores rojo, amarillo, verde y azul; y las formas circular, triangular, cuadrada, rectangular, flechas, diamante y semi-circular. Asimismo cada país tiene sus propias normativas en cuanto a la señalética de tráfico, divida en las categorías de reglamentación, prevención e información (Lillo et al, 2015;Fleyeh et al, 2013;Nguyen et al, 2014;Salti et al, 2015).…”

Section: Bases De Datosunclassified

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Revista Iberoamericana de Automática e Informática Industrial R

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Traffic sign detection based on AdaBoost color segmentation and SVM classification

Cited by 18 publications

References 10 publications

Traffic Sign Detection System for Locating Road Intersections and Roundabouts: The Chilean Case

Traffic Sign Detection System for Locating Road Intersections and Roundabouts: The Chilean Case

Sistema de detección de señales de tráfico para la localización de intersecciones viales y frenado anticipado

Traffic Sign Detection and Recognition

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