A Hybrid Method Based on Haris Corner and Maximally Stable Extremal Regions for Vehicle Plate Number Detection

Authors

  • Budi Setiyono
  • Dwi Ratna Sulistyaningrum
  • Darmaji Darmaji
  • Komar Baihaqi
  • Wahyu Ardiansyah

Keywords:

Smart city, License plate detection, Harris Corner Method, Maximally Stable Extremal Regions method, matching and alignment

Abstract

An intelligent transportation system (ITS) is a crucial element of a smart city, and it includes the capability to identify vehicle license plates. Utilizing digital image processing is a cost-effective method for identification. The tiny size of the number plate is just one of the many unfortunate difficulties with this approach. Hence, this research is crucial, particularly in enhancing the precision of detection. The Harris Corner approach is one way to locate the motor vehicle number plate location. However, the Harris corner method could be more optimal for analyzing moving vehicle videos as input. Since frame-by-frame variations in the video input's lighting, accuracy cannot be maximized. Furthermore, the vehicle and license plate appear significantly smaller due to the camera's distant positioning. Consequently, the authors suggest a hybrid approach using the Maximally Stable Extremal Regions (MSER) method. The Harris Corner and MSER methods will concurrently identify the initial position of the vehicle number plate. Moreover, the initial detection outcomes of the two techniques are compared and adjusted to achieve a more precise determination of the placement of the number plate. The results show that integrating the MSER method into the Harris Corner method yields a hybrid approach that enhances accuracy by 13%. Furthermore, it visually represents the selected number plate with greater accuracy.

References

V. Albino, U. Berardi, and R. M. Dangelico, “Smart cities: definition, deminsion, and performance,” J. Urban Technol., vol. 22, pp. 3–21, 2015, https://doi.org/10.1080/10630732.2014.942092.

S. Fiore et al., “An integrated big and fast data analytics platform for smart urban transportation management,” IEEE Access, vol. 7, pp. 117652–117677, 2019, https://doi.org/10.1109/ACCESS.2019.2936941.

I. Gulati and R. Srinivasan, “Image processing in intelligent traffic management,” Int. J. Recent Technol. Eng., vol. 8, no. 2, special issue 4, pp. 213–218, 2019, https://doi.org/10.35940/ijrte.B1040.0782S419.

K. Iqbal, M. A. Khan, S. Abbas, Z. Hasan, and A. Fatima, “Intelligent transportation system (ITS) for smart-cities using Mamdani fuzzy inference system,” Int. J. Adv. Comput. Sci. Appl., vol. 9, no. 2, pp. 94–105, 2018, https://doi.org/10.14569/IJACSA.2018.090215.

A. Çevik, G. W. Weber, B. M. Eyüboğlu, and K. K. Oğuz, “Voxel-MARS: a method for early detection of Alzheimer’s disease by classification of structural brain MRI,” Ann. Oper. Res., vol. 258, no. 1, pp. 31–57, 2017, https://doi.org/10.1007/s10479-017-2405-7.

A. Crouzil, L. Khoudour, P. Valiere, and D. N. Truong Cong, “Automatic vehicle counting system for traffic monitoring,” J. Electron. Imaging, vol. 25, no. 5, p. 051207, 2016, https://doi.org/10.1117/1.JEI.25.5.051207.

B. Setiyono, D. R. Sulistyaningrum, Soetrisno, and D. W. Wicaksono, “Multi vehicle speed detection using euclidean distance based on video processing,” Int. J. Comput., vol. 18, no. 4, pp. 431–442, 2019, https://doi.org/10.47839/ijc.18.4.1613.

B. Setiyono, R. D. Susanti, D. R. Sulistyaningrum, and I. G. N. Usadha, “Classification and counting of moving vehicle at night with similarity of rear lamp,” J. Phys. Conf. Ser., vol. 1490, no. 1, pp. 1–13, 2020, https://doi.org/10.1088/1742-6596/1490/1/012044.

F. M. D. S. Matos and R. M. C. R. De Souza, “An image vehicle classification method based on edge and PCA applied to blocks,” Conf. Proc. – IEEE Int. Conf. Syst. Man Cybern., pp. 1688–1693, 2012, https://doi.org/10.1109/ICSMC.2012.6377980.

S. Kanagamalliga, S. Vasuki, A. Kanimozhidevi, S. Priyadharshni, and S. Rajeswari, “Tracking and counting the vehicles in night scenes,” Int. J. Inf. Sci. Tech., vol. 4, no. 3, pp. 165–172, 2014, https://doi.org/10.5121/ijist.2014.4320.

S. Padmavathi, C. R. Naveen, and V. Ahalya Kumari, “Vision based vehicle counting for traffic congestion analysis during night time,” Indian J. Sci. Technol., vol. 9, no. 20, pp. 1–6, 2016, https://doi.org/10.17485/ijst/2016/v9i20/91742.

C. Tang, Y. Dong, X. Lin, and W. Xiao, “Multi-field depth vehicle headlight detection by model construction and long trajectory extraction in nighttime city traffic,” Smart Innov. Syst. Technol., vol. 62, pp. 234–246, 2017, https://doi.org/10.1007/978-981-10-3575-3_24.

B. G. Rajagopal, “Detection and pairing of vehicle headlight in night scenes,” Int. J. Res. Eng. IT Soc. Sci., vol. 3, no. 9, pp. 37–53, 2018.

W. Zhang, Q. M. J. Wu, G. Wang, and X. You, “Tracking and pairing vehicle headlight in night scenes,” IEEE Trans. Intell. Transp. Syst., vol. 13, no. 1, pp. 140–153, 2012, https://doi.org/10.1109/TITS.2011.2165338.

R. K. Sathish Kumar, N. Priya, “Vehicle speed computation in night scenes using headlight tracking and pairing,” International Journal of Research in Engineering & Advanced Technology (IJREAT), vol. 2, no. 2, pp. 2–6, 2014.

J. Trivedi, M. S. Devi, and D. Dhara, “Vehicle counting module design in small scale for traffic management in smart city,” Proceedings of the 2018 3rd Int. Conf. Converg. Technol. I2CT 2018, April 2018, pp. 1–6, https://doi.org/10.1109/I2CT.2018.8529506.

M. Z. Zheng and Q. Y. Liu, “Application of LVQ neural network to car license plate recognition,” Proceedings of the 2010 IEEE Int. Conf. Intell. Syst. Knowl. Eng. ISKE 2010, pp. 287–290, 2010, https://doi.org/10.1109/ISKE.2010.5680862.

A. P. Nagare, “License plate character recognition system using neural network,” Int J Comput Appl, vol. 25, no. 10, pp. 36–39, 2011. https://doi.org/10.5120/3147-4345/

H. Li, P. Wang, M. You, and C. Shen, “Reading car license plates using deep neural networks,” Image Vis. Comput., vol. 72, pp. 14–23, 2018, https://doi.org/10.1016/j.imavis.2018.02.002.

O. Ibitoye, T. Ejidokun, O. Dada, and O. Omitola, “Convolutional neural network-based license plate recognition techniques: A short overview,” Proceedings of the 2020 Int. Conf. Comput. Sci. Comput. Intell. CSCI 2020, pp. 1529–1532, 2020, https://doi.org/10.1109/CSCI51800.2020.00283.

T. Vetriselvi et al., “Deep learning based license plate number recognition for smart cities,” Comput. Mater. Contin., vol. 70, no. 1, pp. 2049–2064, 2021, https://doi.org/10.32604/cmc.2022.020110.

V. Gnanaprakash, N. Kanthimathi, and N. Saranya, “Automatic number plate recognition using deep learning,” IOP Conf. Ser. Mater. Sci. Eng., vol. 1084, no. 1, p. 012027, 2021, https://doi.org/10.1088/1757-899X/1084/1/012027.

M. Shahidi Zandi and R. Rajabi, “Deep learning based framework for Iranian license plate detection and recognition,” Multimed. Tools Appl., vol. 81, no. 11, pp. 15841–15858, 2022, https://doi.org/10.1007/s11042-022-12023-x.

W. Puarungroj and N. Boonsirisumpun, “Thai license plate recognition based on deep learning,” Procedia Comput. Sci., vol. 135, pp. 214–221, 2018, https://doi.org/10.1016/j.procs.2018.08.168.

B. Setiyono, D. A. Amini, and D. R. Sulistyaningrum, “Number plate recognition on vehicle using YOLO – Darknet,” J. Phys. Conf. Ser., vol. 1821, no. 1, pp. 1–12, 2021, https://doi.org/10.1088/1742-6596/1821/1/012049.

S. Obulakshmi, M. Ramya, and J. Mythili, “License plate detection using YOLO v4,” Int. J. Health Sci. (Qassim)., vol. 6, no. March, pp. 5098–5111, 2022, https://doi.org/10.53730/ijhs.v6nS5.9741.

R. Laroca et al., “A robust real-time automatic license plate recognition based on the YOLO detector,” Proc. Int. Jt. Conf. Neural Networks, vol. 2018, pp. 1–10, 2018, https://doi.org/10.1109/IJCNN.2018.8489629.

S. Ozbay and E. Ercelebi, “Automatic vehicle identification by plate recognition,” World Acad. Sci. Eng. Technol., vol. 9, pp. 222–225, 2005.

K. Horak, J. Klecka, and P. Novacek, “License plate detection using point of interest detectors and descriptors,” Proceedings of the 2016 39th Int. Conf. Telecommun. Signal Process. TSP 2016, June 2016, pp. 484–488, https://doi.org/10.1109/TSP.2016.7760926.

E. E. Etomi and D. U. Onyishi, “Automated number plate recognition system,” Trop. J. Sci. Technol., vol. 2, no. 1, pp. 38–48, 2021, https://doi.org/10.47524/tjst.21.6.

Y. Han, P. Chen, and T. Meng, “Harris corner detection algorithm at sub-pixel level and its application,” Proceedings of the 2015 Int. Conf. Comput. Sci. Eng., vol. 17, no. ICCSE, pp. 133–137, 2015, https://doi.org/10.2991/iccse-15.2015.23.

C. Luo, X. Sun, X. Sun, and J. Song, “Improved Harris corner detection algorithm based on Canny edge detection and Gray difference preprocessing,” J. Phys. Conf. Ser., vol. 1971, no. 1, 2021, https://doi.org/10.1088/1742-6596/1971/1/012088.

F. A. Akbar and H. Maulana, “Detection of Indonesian vehicle plate location using Harris corner feature detector method,” in International Conference on Science and Technology, 2018, vol. 1, no. Icst, pp. 877–881. https://doi.org/10.2991/icst-18.2018.177

Y. B. Li and J. J. Li, “Harris corner detection algorithm based on improved contourlet transform,” Procedia Eng., vol. 15, pp. 2239–2243, 2011, https://doi.org/10.1016/j.proeng.2011.08.419.

G. S. Hsu, J. C. Chen, and Y. Z. Chung, “Application-oriented license plate recognition,” IEEE Trans. Veh. Technol., vol. 62, no. 2, pp. 552–561, 2013, https://doi.org/10.1109/TVT.2012.2226218.

H. Maulana, D. Herumurti, and A. Yuniarti, “Text based maximally stable extremal regions to detect vehicle plate location,” Proceeding of the International Joint Conference on Science and Technology (IJCST), 2017, pp. 450–455.

M. Donoser and H. Bischof, “Efficient Maximally Stable Extremal Region (MSER) tracking,” Proceedings of the IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., 2006, vol. 1, pp. 553–560, https://doi.org/10.1109/CVPR.2006.107.

T. Panchal, H. Patel, and A. Panchal, “License plate detection using Harris corner and character segmentation by integrated approach from an image,” Procedia Comput. Sci., vol. 79, pp. 419–425, 2016, https://doi.org/10.1016/j.procs.2016.03.054.

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Published

2024-10-03

How to Cite

Setiyono, B., Sulistyaningrum, D. R., Darmaji, D., Baihaqi, K., & Ardiansyah, W. (2024). A Hybrid Method Based on Haris Corner and Maximally Stable Extremal Regions for Vehicle Plate Number Detection. International Journal of Computing, 23(3), 379-386. Retrieved from https://computingonline.net/computing/article/view/3656

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