Real time road signs classification

Medici, Paolo; Caraffi, C.; Cardarelli, Elena; Porta, Pier Paolo; Ghisio, G.

doi:10.1109/icves.2008.4640906

Cited by 13 publications

(5 citation statements)

References 7 publications

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“…This is certified, by examining By comparing our results to some already developed methods for automatic road-sign detection and recognition, the proposed technique ensures better detection and classification rates, in most of the examined cases. For example, in Barnes and Zelinsky (2004) the detection accuracy was 90% and the classification accuracy 75%; in Shneier (2005) the rates were 88% and 78% respectively; and in Medici et al (2008) the rates were 74% and 89.1%. Concerning the proposed methodology and especially during the categorization procedure, HOG descriptors are integrated, which lead to very satisfactory results, when compared to ordinary classification procedures.…”

Section: Overview Of the Proposed Systemmentioning

confidence: 97%

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Automatic road sign detecion and classification based on support vector machines and HOG descriptos

Adam¹,

Ioannidis²

2014

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:This paper examines the detection and classification of road signs in color-images acquired by a low cost camera mounted on a moving vehicle. A new method for the detection and classification of road signs is proposed based on color based detection, in order to locate regions of interest. Then, a circular Hough transform is applied to complete detection taking advantage of the shape properties of the road signs. The regions of interest are finally represented using HOG descriptors and are fed into trained Support Vector Machines (SVMs) in order to be recognized. For the training procedure, a database with several training examples depicting Greek road sings has been developed. Many experiments have been conducted and are presented, to measure the efficiency of the proposed methodology especially under adverse weather conditions and poor illumination. For the experiments training datasets consisting of different number of examples were used and the results are presented, along with some possible extensions of this work.

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Section: Overview Of the Proposed Systemmentioning

confidence: 97%

“…In Medici (2008) multi-layer, feedforward neural networks have been trained, through the backpropagation algorithm, and the images were described by the illumination values. Similar publications using NN for the recognition stage are Nguwi and Kouzani (2008) and Eichner and Breckon (2008).…”

Section: Recognitionmentioning

confidence: 99%

Automatic road sign detecion and classification based on support vector machines and HOG descriptos

Adam¹,

Ioannidis²

2014

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…This method is also prevailing for the task of traffic sign recognition. In [60], six Neural Networks have been trained with the back propagation method for six different classes of road signs. Their proposed algorithm was tested on 200 different traffic signs.…”

Section: Recognition Methodsmentioning

confidence: 99%

Detection and recognition of traffic signs inside the attentional visual field of drivers

Zabihi

Beauchemin

et al. 2017

2017 IEEE Intelligent Vehicles Symposium (IV)

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Traffic sign detection and recognition systems are essential components of Advanced Driver Assistance Systems and self-driving vehicles. In this contribution we present a vision-based framework which detects and recognizes traffic signs inside the attentional visual field of drivers. This technique takes advantage of the driver's 3D absolute gaze point obtained through the combined use of a front-view stereo imaging system and a non-contact 3D gaze tracker. We used a linear Support Vector Machine as a classifier and a Histogram of Oriented Gradient as features for detection. Recognition is performed by using Scale Invariant Feature Transforms and color information. Our technique detects and recognizes signs which are in the field of view of the driver and also provides indication when one or more signs have been missed by the driver.

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“…Many road sign recognition systems use Artificial Neural Network (ANN) in the classification process ( [5], [6], [15], [16], [17]). Template matching using cross correlation is also used to identify road sign objects ( [4], [8], [18], [19]).…”

Section: Introductionmentioning

confidence: 99%

Speed Sign Recognition using Shape-based Features

Abukhait¹,

Zyout²,

Mansour³

2013

IJCA

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An efficient shape-based recognition system of U.S. speed limit road signs is presented in this paper. The proposed system accomplishes speed sign detection and recognition processes using three main stages, namely, geometrical-based detection of rectangular road signs, shape-based segmentation and feature extraction, and pattern classification using a K-nearest neighbor classifier (KNN). Twenty shape descriptors are computed for the most discriminative numerals of each detected sign. The proposed system is invariant to scale, rotation, and partial occlusion. The proposed system has been tested in different conditions, including sunny, cloudy, and rainy weather, and the experimental results on 195 speed signs reveals the efficiency of the proposed shape pattern segmentation and feature extraction methods.

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Real time road signs classification

Cited by 13 publications

References 7 publications

Automatic road sign detecion and classification based on support vector machines and HOG descriptos

Automatic road sign detecion and classification based on support vector machines and HOG descriptos

Detection and recognition of traffic signs inside the attentional visual field of drivers

Speed Sign Recognition using Shape-based Features

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