A study on traffic sign recognition in scene image using genetic algorithms and neural networks

Aoyagi, Yuji; Asakura, Toshiyuki

doi:10.1109/iecon.1996.570749

Cited by 105 publications

(38 citation statements)

References 2 publications

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“…Finally, they reported a 98.5% recognition rate for speed limit signs and a 97.2% recognition rate for warning signs. Aoyagi and Asakura [39] also proposed a traffic sign recognition module using Neural Net-works. They classified their recognition category into three classes: the speed sign, other traffic sign, and not a traffic sign.…”

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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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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“…Aoyagi and Asakura in [12] were the first ones, who worked in area of genetic algorithm (GA) for the sign detection. They detected the speed limit signs.…”

Section: Techniques Based On Color Learning Methodsmentioning

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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“…In the recognition stage, various features extracted [17] from different sign images to characterize them according to the extracted features are used as the input data of any recognition tools or techniques such as support vector machine [16], artificial neural network [14,19,22,1]. Different labels are assingned to each input dataset.…”

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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A study on traffic sign recognition in scene image using genetic algorithms and neural networks

Cited by 105 publications

References 2 publications

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

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

An Overview of Traffic Signs Recognition Methods

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

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