Real-time color segmentation of road signs

Benallal, Mohamed Nadjib; Meunier, Jean

doi:10.1109/ccece.2003.1226265

Cited by 101 publications

(51 citation statements)

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“…Bénallal and Meunier [4] developed a computer vision system which is embedded in a car and capable of identifying road signs. Many experiments were carried out with several road signs to study the stability of colours under different illumination conditions.…”

Section: Literature Reviewmentioning

confidence: 99%

Traffic Sign Detection Based On Gabor Filter And BRISK Descriptor

2017

IJRTER

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Abstract-Traffic signs provide drivers important information for security and efficient navigation. Automatic detection of traffic signs inevitably become more popular. In this paper, an efficient method/platform is presented to achieve automatic traffic signs detection. Gabor filter bank and BRISK descriptor are used as keypoint fusion approach for traffic sign detection. Our experiment shows that the system can achieve a high detection rate of 100% for pedestrian and bike crossing traffic signs.Keywords-Intelligent Transport System (ITS), Image Processing, Traffic Sign Detection, Image Classification, Traffic Signs, Gabor Filter, BRISK Descriptor.I. INTRODUCTION Traffic sign detection is a technology by which a vehicle is able to recognize the different road and traffic signs. They are used to regulate, warn, guide or inform road users. A road and traffic sign detection system could in principle be developed as part of an Intelligent Transport Systems (ITS) that continuously monitors the driver, the vehicle, and the road in order, for example, to inform the driver in time about upcoming decision points regarding navigation and potentially risky traffic situations. The identification of road signs can be carried out by two main stages: detection and recognition. In 'detection' research groups are categorised into three groups. The first group of researchers believes that traffic sign colours are important information by which traffic signs can be detected and classified. The second group believes that detection of traffic signs can be achieved by traffic sign shape only, and the third believes that colour together with shape make the backbone for any road sign detection. Thus, there are three major approaches to detecting traffic signs: detection using colour information, detection using shape information, and detection using both colour and shape information. Although traffic signs are apparent and have several obvious characteristics, some conditions may prevent driver perceiving them. For instance, at night or in bad lighting conditions drivers are less likely to notice the traffic signs. Some distracting events on road may result in a skip of signs. Moreover, sometimes only the driver himself is not able to notice the signs due to lack of concentration. Driving needs continuous processing of visual information from the road. To avoid accident, driver needs to monitor a lot of traffic signs. For this purpose traffic signs play an important role to provide information about traffic and road conditions which is necessary for a driver to accomplish a collision free driving environment.Keypoint based method is used for traffic sign detection it possesses two steps to get detected in your reference images. This detection methods are usually deals with the point features. First step keypoint generation plays a fundamental role in many applications of computer vision, such as image registration, object recognition, simultaneous localization and mapping (SLAM) and so on. The favorable feature detector, descriptor an...

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Section: Literature Reviewmentioning

confidence: 99%

Traffic Sign Detection Based On Gabor Filter And BRISK Descriptor

2017

IJRTER

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“…For example, authors in [10] tested YUV and RGB color spaces and finally took the second one because of its lower number of false positives and lower computational time. Moreover, Benallal and Meunier [11] have studied the behavior of the RGB components of many traffic signs and have found this method reliable for road signs segmentation. Many other researchers also focused on RGB segmentation such as [12], [13], and [14].…”

Section: Rgb Color Spacementioning

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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“…They concluded that the color saturation shows significant change when the videos are taken from different devices. Some authors [10] studied the effect of light on the color of traffic signs during day and night and concluded that the color of roadside image could get distorted due to light and this may affect the quality of images. However, they emphasized that outdoor illumination does not affect the RGB component differences for traffic signs.…”

Section: Techniques Based On Color Thresholdingmentioning

confidence: 99%

An Overview of Traffic Signs Recognition Methods

Dewan¹,

Vig²,

Shukla³

et al. 2017

IJCA

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Image processing has many areas of applications like in metrological prediction of weather conditions, medical science, artificial intelligence, robotics etc. Traffic signs recognition system is in fact the most happening area of research these days. As the world is moving towards the driverless vehicles, a more automated world is the utmost requirement for improving the road safety. Such systems can help the drivers on signs that they may not have noticed beforehand. These systems are so designed so that they consume less power and hence can be efficiently implemented on hardware. FPGA's are preferred over CPU and GPU due to its low cost and power, prototyping applications and next levels to ASIC's development. In this paper we have quoted some basic challenges in traffic signs recognition methods and summarized the various detection and recognition techniques for traffic signs. This paper divided the various methods into three categories: color-based, shape-based and learning-based. We have concluded that the Xilinx System Generator is the best tool while implementing on FPGA's. It is the fastest resource estimation tool in order to take full advantage of FPGA's resources. Finally, the hardware perspective of traffic signs implementation is briefly examined.

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Real-time color segmentation of road signs

Cited by 101 publications

References 1 publication

Traffic Sign Detection Based On Gabor Filter And BRISK Descriptor

Traffic Sign Detection Based On Gabor Filter And BRISK Descriptor

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

An Overview of Traffic Signs Recognition Methods

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