Pattern Recognition on Vehicle Number Plates Using a Fast Match Algorithm

Authors

  • Cahaya Jatmoko Universitas Dian Nuswantoro
  • Daurat Sinaga Universitas Dian Nuswantoro
  • Edi Sugiarto Universitas Dian Nuswantoro
  • Nur Rokhman Universitas Dian Nuswantoro
  • Heru Lestiawan Universitas Dian Nuswantoro

DOI:

https://doi.org/10.33633/jais.v6i2.4625

Abstract

Computer Vision was the fast developing apps in the world, it is make people make a lot of new algorithm. Before we can use in out app, we need to test the algorithm to make sure how effective and optimal the algorithm to solve every case we given. A lot of traffic system has implemented computer vision, they need fast and can work in every condition, because every vehicle who pass needs to be recognized. In this research Fast Match algorithm was chosen because they can solve some test and make a lot of image have a similarity with the template. It makes accuracy of the data can be achieved with this algorithm. For example on of the sample was have a SAD point for 0.5 and Overlap Error for 0.5 and can run in standard computer just for a couple second. It makes the template and the original image has a little similarity.

References

S. Saha, S. Basu, M. Nasipuri, and D. K. Basu, “A Hough Transform based Technique for Text Segmentation,” J. Comput., vol. 2, no. 2, pp. 134–141, Feb. 2010.

A. Mousa, “Canny Edge-Detection Based Vehicle Plate Recognition,” Int. J. Signal Process. Image Process. Pattern Recognit., vol. 5, no. 3, pp. 1–8, 2012.

I. Kusumadewi, C. A. Sari, D. R. I. Moses Setiadi, and E. H. Rachmawanto, “License Number Plate Recognition using Template Matching and Bounding Box Method,” in Journal of Physics: Conference Series, 2019, vol. 1201, no. 1.

M. M. Shidore and S. P. Narote, “Number Plate Recognition for Indian Vehicles,” Acad. Res. Int., vol. 4, no. 3, pp. 48–55, 2013.

F. Patel, J. Solanki, V. Rajguru, and A. Saxena, “Recognition of Vehicle Number Plate Using Image Processing Technique,” Adv. Emerg. Med., vol. 7, no. 1, pp. 2–8, 2018.

U. Dwivedi, P. Rajput, and M. K. Sharma, “License Plate Recognition System for Moving Vehicles Using Laplacian Edge Detector and Feature Extraction,” Int. Res. J. Eng. Technol., vol. 4, no. 3, pp. 407–412, 2017.

R. Panahi and I. Gholampour, “Accurate Detection and Recognition of Dirty Vehicle Plate Numbers for High-Speed Applications,” IEEE Trans. Intell. Transp. Syst., vol. 18, no. 4, pp. 767–779, Apr. 2017.

Z. Zhang and C. Wang, “The Research of Vehicle Plate Recognition Technical Based on BP Neural Network,” AASRI Procedia, vol. 1, pp. 74–81, 2012.

M. T. Qadri and M. Asif, “Automatic Number Plate Recognition System for Vehicle Identification Using Optical Character Recognition,” in 2009 International Conference on Education Technology and Computer, 2009, pp. 335–338.

S. Korman, D. Reichman, G. Tsur, and S. Avidan, “FasT-Match: Fast Affine Template Matching,” in 2013 IEEE Conference on Computer Vision and Pattern Recognition, 2013, pp. 2331–2338.

J. Kim, J. K. Lee, and K. M. Lee, “Accurate Image Super-Resolution Using Very Deep Convolutional Networks,” in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, vol. 2016-Decem, pp. 1646–1654.

S. Caraiman et al., “Computer Vision for the Visually Impaired: the Sound of Vision System,” in 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), 2017, vol. 2018-Janua, pp. 1480–1489.

M. M. Azad, M. M. Hasan, and M. N. K, “Color Image Processing in Digital Image,” Int. J. New Technol. Res., vol. 3, no. 3, pp. 56–62, 2017.

X. Jun-bo, “Template matching algorithm based on gradient search,” in 2014 International Conference on Mechatronics and Control (ICMC), 2014, no. Icmc, pp. 1472–1475.

Y. M. Fouda, “A Robust Template Matching Algorithm Based on Reducing Dimensions,” J. Signal Inf. Process., vol. 06, no. 02, pp. 109–122, 2015.

H. Niitsuma and T. Maruyama, “Sum of Absolute Difference Implementations for Image Processing on FPGAs,” in 2010 International Conference on Field Programmable Logic and Applications, 2010, no. D, pp. 167–170.

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Published

2021-12-06

Issue

Vol. 6 No. 2 (2021): Journal of Applied Intelligent System

Section

Articles

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