Abstract
This paper proposes an efficient feature points for traffic sign recognition (TSR) system. This system composed of adaptive thresholding method based on RGB color detection, shape validation, feature extraction and adaptive neuro fuzzy inference system (ANFIS). There are some important issues for real-time TSR system such as; lighting conditions, faded color traffic signs and weather conditions. The proposed adaptive thresholding method overcomes various illumination and poor contrast color. Features play main role in TSR system. The significant feature points such as termination points, bifurcation points and crossing points are proposed. This proposes feature points provide good accuracy in TSR system. Lastly ANFIS is used to recognize the proposed feature points. This system showed that this proposed method can achieve cloudy and drizzle rain condition. In this system, this proposed method is used to evaluate on Myanmar Traffic Sign data.
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References
Deshpande, A.V.: A brief overview of traffic sign detection methods. Int. J. Eng. Res. 141–144 (2016)
Win, W.S., Myint, T.: Detection and recognition of Myanmar traffic signs from video. In: Proceedings of 2015 International Conference on Future Computational Technologies, (ICFCT’2015), Singapore. ISBN 978-93-84468-20-0
Billah, M., Waheed, S., Ahmed, K., Hanifa, A.: Real time traffic sign detection and recognition using adaptive neuro fuzzy inference system. Commun. Appl. Electron. (CAE) 3(1) (2015). ISSN 2394 – 4714
Mogelmose, A., Liu, D., Trivedi, M.M.: Detection of US traffic signs. IEEE Trans. Intell. Transp. Syst. (2015)
Agrawal, S., Chaurasiya, R.K.: Ensemble of SVM for accurate traffic sign detection and recognition. In: ICGSP, Singapore (2017)
Berkaya, S.K., Gunduz, H., Ozsen, O., Akinlar, C., Akinlar, S.: On circular traffic sign detection and recognition. Expert Syst. Appl. 48, 67–75 (2016)
Daraghmi, Y.A., Hasasneh, A.M.: Accurate real-time traffic sign recognition based on the connected component labeling and the color histogram algorithms. In: International Conference on Signal Processing (2015)
Aparna, S., Abraham, D.: Multiple traffic sign detection and recognition using SVM. Int. J. Adv. Res. Comput. Sci. Manag. Stud. 3(8) (2015)
Islam, Kh.T., Raj, R.G.: Recognition of traffic sign based on bag-of-words and artificial neural network. Symmetry 138 (2017)
Islam, Kh.T., Raj, R.G.: “Real-time (vision-based) road sign recognition using an artificial neural network”, Sensors, 17(4), p. 853, 2017
Sharma, P., Abrol, P.: Color based image segmentation using adaptive thresholding. Int. J. Sci. Tech. Adv. 2(3), 151–156 (2016)
Kumar, G., Bhatia, P.K.: A detailed review of feature extraction in image processing systems. In: Advanced Computing & Communication Technologies (ACCT), Feb 2014
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Phu, K.T., Oo, L.L. (2019). Efficient Feature Points for Myanmar Traffic Sign Recognition. In: Lee, R. (eds) Computer and Information Science. ICIS 2018. Studies in Computational Intelligence, vol 791. Springer, Cham. https://doi.org/10.1007/978-3-319-98693-7_10
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DOI: https://doi.org/10.1007/978-3-319-98693-7_10
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