Robust traffic signs detection by means of vision and V2I communications

García-Garrido, Miguel Ángel; Ocaña, Manuel; Llorca, David Fernández; Sotelo, Miguel Ángel; Arroyo, Estefanía; Llamazares, Ángel

doi:10.1109/itsc.2011.6082844

Cited by 26 publications

(19 citation statements)

References 26 publications

(35 reference statements)

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“…A comparison of previous studies in detecting the traffic sign is given in Table 4. From Table 5, it can be observed that SVM used in [44] has the highest recall rate with an overall good accuracy of over 90%. 327 signs out of 340 signs are correctly classified.…”

Section: Performance Comparison Of Svm Based Recognitionmentioning

confidence: 97%

“…Support Vector Machine (SVM) is another popular method used by the researchers which is robust against illumination and rotation with a very high accuracy. Yang et al [33] and García-Garrido et al [34] used SVM with Gaussian Kernels for the recognition whereas Park and Kim [35] used an advanced SVM technique that improved the computational time and the accuracy rate for gray scale images.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

An Automatic Traffic Sign Detection and Recognition System Based on Colour Segmentation, Shape Matching, and SVM

Wali

Hannan

Hussain

et al. 2015

Mathematical Problems in Engineering

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The main objective of this study is to develop an efficient TSDR system which contains an enriched dataset of Malaysian traffic signs. The developed technique is invariant in variable lighting, rotation, translation, and viewing angle and has a low computational time with low false positive rate. The development of the system has three working stages: image preprocessing, detection, and recognition. The system demonstration using a RGB colour segmentation and shape matching followed by support vector machine (SVM) classifier led to promising results with respect to the accuracy of 95.71%, false positive rate (0.9%), and processing time (0.43 s). The area under the receiver operating characteristic (ROC) curves was introduced to statistically evaluate the recognition performance. The accuracy of the developed system is relatively high and the computational time is relatively low which will be helpful for classifying traffic signs especially on high ways around Malaysia. The low false positive rate will increase the system stability and reliability on real-time application.

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Section: Performance Comparison Of Svm Based Recognitionmentioning

confidence: 97%

Section: Related Workmentioning

confidence: 99%

An Automatic Traffic Sign Detection and Recognition System Based on Colour Segmentation, Shape Matching, and SVM

Wali

Hannan

Hussain

et al. 2015

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…Overall Accuracy (%) Processing Time (s) [1] 97.60 - [15] 95.71 0.43 [17] 93.60 - [24] 98.62 0.36 [35] 95.20 - [37] 92.47 - [57] 90.27 0.35 [58] 86.70 -Proposed method 99.00 0.28 Figure 15. Real-time experimental results.…”

Section: Referencementioning

confidence: 99%

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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“…SVM, Neural Network, Hough Transform are the methods that is used by the researchers to minimize the effect of lighting on TSDR system. Neural Network [14][15][16][17][18] AdaBoost [19,20], SVM method for classification [21][22][23][24][25][26] Self-organizing maps (SOM) [27], joint transform correlation (JTC) [28] are the key methods used by different researcher to minimize the effect of various lighting. Many new methods like MSER based HOG [29], The Karhunen-Loeve transform [30], Low Rank Matrix Recovery (LRMR) [31], Fuzzy c means (FCM) [32] are the other machine learning algorithms introduced by the researchers.…”

Section: Current Trends and Challengesmentioning

confidence: 99%

Comparative Survey on Traffic Sign Detection and Recognition: a Review

Wali

Hannan

Hussain

et al. 2015

ELECTROTECHNICAL REVIEW

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Developing real-time Advanced Driver Assistance Systems (ADAS) based on video aiming to extract reliable vehicle state information has attracted a lot of attention during the past decades. This ADAS system includes inter-vehicle communication, driver behavioral monitoring, and human-machine interactions. In these systems, robust and reliable traffic sign detection and recognition (TSDR) technique is a critical step for ensuring vehicle safety. This paper provides a comprehensive survey on traffic sign detection and recognition system based on image and video data. Our main focus is to present the current trends and challenges in the field of developing an efficient TSDR system followed by a detail comparative study between different renowned methods used by various researchers. Finally, conclusion followed by some future suggestion is provided to develop an efficient TSDR system is provided. This survey will hopefully lead to develop an effective traffic sign detection and recognition system which will ensure driver safety in future. Streszczenie. System ADAS (Advanced Driver Assistance System) obejmuje także metody rozpoznawania znaków drogowych. W artykule przedstawiono przegląd metod detekcji i rozpoznawania znaków drogowych bazujących na obrazie video. W artykule dokonano oceny istniejących metod oraz zaproponowano środki poprawy ich efektywności. Studium porównawcze metod detekcji i rozpoznawania znaków drogowych

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Robust traffic signs detection by means of vision and V2I communications

Cited by 26 publications

References 26 publications

An Automatic Traffic Sign Detection and Recognition System Based on Colour Segmentation, Shape Matching, and SVM

An Automatic Traffic Sign Detection and Recognition System Based on Colour Segmentation, Shape Matching, and SVM

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

Comparative Survey on Traffic Sign Detection and Recognition: a Review

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