Road Sign Detection and Recognition using Colour Segmentation, Shape Analysis and Template Matching

Malik, Rayaz A.; Khurshid, Javaid; Ahmad, Safwan

doi:10.1109/icmlc.2007.4370763

Cited by 45 publications

(17 citation statements)

References 12 publications

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“…The adjusted image is then processed similar to P 1 . For P 3 we used the intensity of the red component from the color image as the gray level to get a gray image. The remaining steps are the same as for P 1 .…”

Section: Data Collection Size Analysis and Preprocessingmentioning

confidence: 99%

“…These and other common issues have been discussed in related studies [1]. Researchers have applied a variety of preprocessing techniques based on color processing [3,4], shape analysis [3][4][5], along with numerous classification and recognition techniques ranging from template matching algorithms [3,6], Radial Basis Function (RBF) Network [7,8], Multilayer Perceptron and other Neural Networks [7,8], Genetic Algorithms [9], Fuzzy Logic [10] to the applications of Support Vector Machines [11] and signal processing-based transformation-specific classifiers.…”

Section: Introductionmentioning

confidence: 99%

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Analysis of Speed Sign Classification Algorithms Using Shape Based Segmentation of Binary Images

Muhammad

Lavesson

Davidsson

et al. 2009

Computer Analysis of Images and Patterns

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Abstract. Traffic Sign Recognition is a widely studied problem and its dynamic nature calls for the application of a broad range of preprocessing, segmentation, and recognition techniques but few databases are available for evaluation. We have produced a database consisting of 1,300 images captured by a video camera. On this database we have conducted a systematic experimental study. We used four different preprocessing techniques and designed a generic speed sign segmentation algorithm. Then we selected a range of contemporary speed sign classification algorithms using shape based segmented binary images for training and evaluated their results using four metrics, including accuracy and processing speed. The results indicate that Naive Bayes and Random Forest seem particularly well suited for this recognition task. Moreover, we show that two specific preprocessing techniques appear to provide a better basis for concept learning than the others.

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Section: Data Collection Size Analysis and Preprocessingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Analysis of Speed Sign Classification Algorithms Using Shape Based Segmentation of Binary Images

Muhammad

Lavesson

Davidsson

et al. 2009

Computer Analysis of Images and Patterns

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“…Following this, they were segmented using a set of fuzzy rules depending on the hue and saturation values of each pixel in the HSV color space. According to [20], the HSV color space is appropriate for color segmentation since the Hue component of this space is invariant to changing light intensity.…”

Section: Angle Colormentioning

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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“…Particularly, in [10], after a color-based segmentation in HSI space, a connected component labeling process is carried out in order to find objects in the image. Once the objects are found, a fuzzy shape descriptor is applied to determine whether the road sign is circular or triangular.…”

Section: Introductionmentioning

confidence: 99%

Detection and tracking of traffic signs using a recursive Bayesian decision framework

Marinas

Salgado

Arróspide

et al. 2011

2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC)

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Abstract-In this paper we propose a new method for the automatic detection and tracking of road traffic signs using an on-board single camera. This method aims to increase the reliability of the detections such that it can boost the performance of any traffic sign recognition scheme. The proposed approach exploits a combination of different features, such as color, appearance, and tracking information. This information is introduced into a recursive Bayesian decision framework, in which prior probabilities are dynamically adapted to tracking results. This decision scheme obtains a number of candidate regions in the image, according to their HS (Hue-Saturation). Finally, a Kalman filter with an adaptive noise tuning provides the required time and spatial coherence to the estimates. Results have shown that the proposed method achieves high detection rates in challenging scenarios, including illumination changes, rapid motion and significant perspective distortion.

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Road Sign Detection and Recognition using Colour Segmentation, Shape Analysis and Template Matching

Cited by 45 publications

References 12 publications

Analysis of Speed Sign Classification Algorithms Using Shape Based Segmentation of Binary Images

Analysis of Speed Sign Classification Algorithms Using Shape Based Segmentation of Binary Images

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

Detection and tracking of traffic signs using a recursive Bayesian decision framework

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