Detection and Classification of Signage’s from Random Mobile Videos Using Local Binary Patterns

Full Text (PDF, 662KB), PP.52-59

Views: 0 Downloads: 0


Shivanand S. Gornale 1,* Ashvini K Babaleshwar 1 Pravin L Yannawar 2

1. Department of Computer Science, Rani Channamma University, Belagavi, Karnataka.

2. Dr. Babasaheb Ambedkar Marathwada University, Aurangabad, (MS) India.

* Corresponding author.


Received: 12 Sep. 2017 / Revised: 24 Nov. 2017 / Accepted: 10 Jan. 2018 / Published: 8 Feb. 2018

Index Terms

Traffic Sign Detection, Local Binary Pattern (LBP), Video Tracking, Signage’s


The Traffic-Sign detection and recognition plays significant role in the design of autonomous driverless cars for navigation purpose as well as to assist a driver for alerting and educating him about the tracked signage on the road side. The main objective of this paper is to highlight an automatic process of detection of Region Of Interest (ROI) which marks or isolates signage’s from color video streams and performs classification of automatically detected signage’s based on support vector machine (SVM) classifiers trained over Local Binary Pattern (LBP) features. The training dataset was captured through 13 mega pixel mobile camera in different illumination and light conditions and due to randomness the data base complexity is very high. The robustness of the proposed system is measured on the bases its of capability of automatic detection and classification of ROI in a given video stream and backed with a comprehensive result analysis presented in this piece of work.

Cite This Paper

Shivanand S Gornale, Ashvini K Babaleshwar, Pravin L Yannawar," Detection and Classification of Signage’s from Random Mobile Videos Using Local Binary Patterns", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.10, No.2, pp. 52-59, 2018. DOI: 10.5815/ijigsp.2018.02.06


[1]Karthiga P. L, S. Md. MansoorRoomi, Kowsalya.J, “Traffic-Sign Recognition For An Intelligent Vehicle/Driver Assistant System Using Hog”, Computer Science & Engineering: An International Journal (CSEIJ), vol.6, No.1, pp: 16-23, February 2016.

[2]Vishal R. Deshmukh, G. K. Patnaik, M. E. Patil, “Real-Time Traffic Sign Recognition System based on Colour Image Segmentation”, International Journal of Computer Applications (0975 – 8887) Vol.83, No3, pp: 30-35, December 2013

[3]Md. Safaet Hossain, Zakir Hyder, “Traffic Road Sign Detection and Recognition for Automotive Vehicles”, International Journal of Computer Applications (0975 – 8887) Vol.120, No.24, pp: 11-15, June 2015.

[4]Rabia Malik, Javaid Khurshid, Sana Nazir Ahmad, “Road Sign Detection And Recognition Using Colour Segmentation, Shape Analysis and Template Matching”, Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, Hong Kong, pp:3556-3560, 19-22 August 2007.

[5]Jack Greenhalgh and MajidMirmehdi, “Real-Time Detection and Recognition of Road Traffic Signs”, IEEE Transactions on Intelligent Transportation Systems, Vol.13, No.4, pp:1498-1506, December 2012.

[6]Saturnino Maldonado-Bascón, Sergio Lafuente-Arroyo , Pedro Gil-Jiménez,Hilario Gómez-Moreno, Francisco Lopez-Ferreras, “Road-Sign Detection and Recognition Based on Support Vector Machines”, IEEE Transactions On Intelligent Transportation Systems, Vol.8, No.2, pp: 264-278, June 2007.

[7]R. Belaroussi, P. Foucher , J.-P. Tarel, B. Soheilian , P. Charbonnier , N. Paparoditis, “Road Sign Detection in Images: A Case Study”. 20th International Conference on Pattern Recognition (ICPR), pp: 484-488, 07 October 2010.

[8]Zhiyong Huang, Yuanlong Yu, Jason Gu, and Huaping Liu, “An Efficient Method for Traffic Sign Recognition Based on Extreme Learning Machine”, IEEE Transactions On Cybernetics, Vol.47, Issue.4, pp: 920-933, April 2017.

[9]Ojala, T., Pietikainen, M. and Maenpaa, T. (2002), Multiresolution Gray-scale and Rotation Invariant Texture Classification with Local Binary Patterns. IEEE Trans. Pattern Analysis and Machine Intelligence 24(7): 971-987.

[10]M. Srinivasa Rao, V.Vijaya Kumar, Mhm Krishna Prasad, “Texture Classification based on First order Local Ternary Direction pattern”, International Journal of Image, Graphics and Signal Processsing, 2017, 2, pp:46-54.

[11]K. Srinivasa  Reddy, V.Vijaya Kumar, B. Eswara Reddy, “Face Recognition Based on Texture Features using Local Ternary Patterns”, International Journal of Image, Graphics and Signal Processsing, 2015, 10, pp:37-46.

[12]Alireza Tofighi, Nima Khairdoost, S. Amirhassan Monadjemi, Kamal Jamshidi, “A Robust Face Recognition Sysytem in Image and Viddeo ”, International Journal of Image, Graphics and Signal Processsing, 2014, 8, pp:1-11.

[13]Dolly Choudhary, Ajay Kumar Singh, Shamik Tiwari, “a Statistical Approach for Iris Recognition Using K-NN Classifier”, International Journal of Image, Graphics and Signal Processsing, 2013, 4, pp:46-52.

[14]S.S. Gornale, Basavanna M, Kruti R, “Fingerprint Based Gender Classification Using Local Binary Pattern”, International Journal of Computational Intelligence Research, ISSN 0973-1873 Vol.13, No 2(2017), pp:261-271, @Research Indian Publication.

[15]S.S.Gornale, Pooja U. Patravali, “Medical Imaging in Clinical Applications: Algorithmic and Computer based approaches”, Basic Chapter, “Engineering and Technology: Latest Progress”, pp: 65-104, ISBN 978-81-932850-2-2, 2017.

[16]Shivanand.S.Gornale, Pooja U. Patravali, Kiran S. Marathe, Prakash S. Hiremath, “Determination of Osteoarthritis using Histogram of Oriented Gradients and Multiclass SVM”, International Journal of Image, Graphics and Signal Processsing, Vol.9, No.12, pp.41-49, 2017, DOI:10.5815/ijigsp.2017.12.05.