Real-Time Automatic Road Sign Detection

Wu, Jianping; Si, Maoxin; Tan, Fangyong; Gu, Chenglin

doi:10.1109/icig.2009.66

Cited by 4 publications

(5 citation statements)

References 18 publications

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“…All researchers are implementing their methods to achieve a common goal [ 25 ]. Some researchers have done the detection [ 26 , 27 , 28 ] part, some are tracking the detection and a few have described effective recognition parts [ 29 , 30 ]. According to Paclík et al [ 31 ], an automated traffic sign detection system was introduced for the first time in Japan in 1984.…”

Section: Related Workmentioning

confidence: 99%

Real-Time (Vision-Based) Road Sign Recognition Using an Artificial Neural Network

Islam

Raj

2017

Sensors

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Road sign recognition is a driver support function that can be used to notify and warn the driver by showing the restrictions that may be effective on the current stretch of road. Examples for such regulations are ‘traffic light ahead’ or ‘pedestrian crossing’ indications. The present investigation targets the recognition of Malaysian road and traffic signs in real-time. Real-time video is taken by a digital camera from a moving vehicle and real world road signs are then extracted using vision-only information. The system is based on two stages, one performs the detection and another one is for recognition. In the first stage, a hybrid color segmentation algorithm has been developed and tested. In the second stage, an introduced robust custom feature extraction method is used for the first time in a road sign recognition approach. Finally, a multilayer artificial neural network (ANN) has been created to recognize and interpret various road signs. It is robust because it has been tested on both standard and non-standard road signs with significant recognition accuracy. This proposed system achieved an average of 99.90% accuracy with 99.90% of sensitivity, 99.90% of specificity, 99.90% of f-measure, and 0.001 of false positive rate (FPR) with 0.3 s computational time. This low FPR can increase the system stability and dependability in real-time applications.

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Section: Related Workmentioning

confidence: 99%

Real-Time (Vision-Based) Road Sign Recognition Using an Artificial Neural Network

Islam

Raj

2017

Sensors

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“…Based on execution times available in literature, such as in [Chen 2009, Leu 2011, Cerone 2009, Wu 2009, Jung 2005, and confidential data provided by commercial manufacturers, execution times of assistance tasks are scaled to be comparable. As the prototype implementation of the advocated virtualization scheme exploits Xilinx MicroBlaze soft-core processors with fairly limited performance, explicit task execution times are not given.…”

Section: Timing Constraints Task Execution Timesmentioning

confidence: 99%

Design Concepts for a Virtualizable Embedded MPSoC Architecture

Biedermann¹

2014

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“…Round road sign detection on Chinese highways was proposed by Wu et al [31]. The limitation of [31] is it can only apply to the detection and recognition of round shaped road signs and cannot identify any other shaped traffic sign.…”

Section: Related Workmentioning

confidence: 99%

“…Some researchers carried out the investigation to just detect traffic signs without the use of classification [31,32], whereby some researchers were focused on detection and recognition of the traffic signs [33]. Round road sign detection on Chinese highways was proposed by Wu et al [31]. The limitation of [31] is it can only apply to the detection and recognition of round shaped road signs and cannot identify any other shaped traffic sign.…”

Section: Related Workmentioning

confidence: 99%

“…Their proposed system failed to recognize traffic signs in two instances, when the traffic sign is intensively illuminated and when the traffic sign has partially been occluded with the same color object. Some researchers carried out the investigation to just detect traffic signs without the use of classification [31,32], whereby some researchers were focused on detection and recognition of the traffic signs [33]. Round road sign detection on Chinese highways was proposed by Wu et al [31].…”

Section: Related Workmentioning

confidence: 99%

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Recognition of Traffic Sign Based on Bag-of-Words and Artificial Neural Network

Islam¹,

Raj

Mujtaba³

2017

Symmetry

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Abstract:The traffic sign recognition system is a support system that can be useful to give notification and warning to drivers. It may be effective for traffic conditions on the current road traffic system. A robust artificial intelligence based traffic sign recognition system can support the driver and significantly reduce driving risk and injury. It performs by recognizing and interpreting various traffic sign using vision-based information. This study aims to recognize the wellmaintained, un-maintained, standard, and non-standard traffic signs using the Bag-of-Words and the Artificial Neural Network techniques. This research work employs a Bag-of-Words model on the Speeded Up Robust Features descriptors of the road traffic signs. A robust classifier Artificial Neural Network has been employed to recognize the traffic sign in its respective class. The proposed system has been trained and tested to determine the suitable neural network architecture. The experimental results showed high accuracy of classification of traffic signs including complex background images. The proposed traffic sign detection and recognition system obtained 99.00% classification accuracy with a 1.00% false positive rate. For real-time implementation and deployment, this marginal false positive rate may increase reliability and stability of the proposed system.

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Real-Time Automatic Road Sign Detection

Cited by 4 publications

References 18 publications

Real-Time (Vision-Based) Road Sign Recognition Using an Artificial Neural Network

Real-Time (Vision-Based) Road Sign Recognition Using an Artificial Neural Network

Design Concepts for a Virtualizable Embedded MPSoC Architecture

Recognition of Traffic Sign Based on Bag-of-Words and Artificial Neural Network

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