KIPS Transactions on Software and Data Engineering (정보처리학회논문지:소프트웨어 및 데이터공학)

Korea Information Processing Society (한국정보처리학회)
권호 표지
DOI QR코드

DOI QR Code

Real-Time Vehicle License Plate Recognition System Using Adaptive Heuristic Segmentation Algorithm

적응 휴리스틱 분할 알고리즘을 이용한 실시간 차량 번호판 인식 시스템

  • 진문용 (현대모비스 연구개발본부) ;
  • 박종빈 (전북대학교 전자정보공학부) ;
  • 이동석 (전북대학교 전자정보공학부) ;
  • 박동선 (전북대학교 전자공학부)
  • Received : 2014.02.28
  • Accepted : 2014.07.28
  • Published : 2014.09.30
Download PDF
⟨ Previous Next ⟩

Abstract

The LPR(License plate recognition) system has been developed to efficient control for complex traffic environment and currently be used in many places. However, because of light, noise, background changes, environmental changes, damaged plate, it only works limited environment, so it is difficult to use in real-time. This paper presents a heuristic segmentation algorithm for robust to noise and illumination changes and introduce a real-time license plate recognition system using it. In first step, We detect the plate utilized Haar-like feature and Adaboost. This method is possible to rapid detection used integral image and cascade structure. Second step, we determine the type of license plate with adaptive histogram equalization, bilateral filtering for denoise and segment accurate character based on adaptive threshold, pixel projection and associated with the prior knowledge. The last step is character recognition that used histogram of oriented gradients (HOG) and multi-layer perceptron(MLP) for number recognition and support vector machine(SVM) for number and Korean character classifier respectively. The experimental results show license plate detection rate of 94.29%, license plate false alarm rate of 2.94%. In character segmentation method, character hit rate is 97.23% and character false alarm rate is 1.37%. And in character recognition, the average character recognition rate is 98.38%. Total average running time in our proposed method is 140ms. It is possible to be real-time system with efficiency and robustness.

차량 번호판 인식 시스템은 복잡한 교통환경의 효율적 관리를 위해 발전되어 현재 많은 곳에 사용되고 있다. 그러나 조명, 잡음, 배경변화, 번호판 훼손 등 환경변화에 큰 영향을 받기 때문에 제한된 환경에서만 동작하며, 실시간으로 사용하기 어렵다. 본 논문에서는 조명변화와 잡음에 강건하며 빠른 번호판 인식을 위한 휴리스틱 분할 알고리즘 및 이를 이용한 실시간 번호판 인식 시스템을 제안한다. 첫 번째 단계는 Haar-like 특징과 Adaboost를 이용하여 번호판을 검출한다. 이 방법은 적분영상을 이용하며 케스케이드 구조로 구성되어 있어 빠른 검출이 가능하다. 두 번째 단계에서 적응 히스토그램 평활화 방법과 노이즈를 경감시키는 바이레터럴 필터를 이용하여 번호판의 종류를 결정한 후, 번호판 종류에 따라 적분영상을 이용한 적응 이진화, 픽셀 프로젝션, 사전지식 등을 기반으로 빠르고 정확한 문자 분할을 한다. 세번째 단계에서는 HOG와 신경망 알고리즘을 이용하여 숫자를 인식하고, SVM을 이용해 한글을 인식한다. 실험결과는 번호판검출에 94.29%의 검출률, 2.94%의 오경보율을 보이며, 문자분할에서는 검출률 97.23%, 2.94%의 오경보율을 보였다. 문자인식에서 평균 인식률은 98.38%이다. 평균 운용시간은 140ms으로 빠르고 강인한 실시간 시스템을 만들 수 있다.

Keywords

References

  1. V. Kamat and S. Ganesan, "An efficient implementation of the Hough transform for detecting vehicle license plates using DSPs," Proc. Real-Time Tech. Applicat. Symp., pp.58-59, 1995.
  2. P. Wu, H.-H. Chen, R.-J. Wu, and D.-F. Shen, "License plate extraction in low resolution video," Proc. Int. Conf. Pattern Recognit., Vol.1, pp.824-827, 2006.
  3. K. Deb and J. Kang-Hyun, "HSI color based vehicle license plate detection," Control, Automation and Systems, ICCAS 2008, International Conference on, pp.687-691, 2008.
  4. Md. M. K. Sarker, "License Plate Detection Using Cascade AdaBoost and Heuristic Energy Map," Master's degree thesis, Chonbuk National University, 2013.
  5. L. Dlagnekov, "License Plate Detection Using AdaBoost," San Diego, CA: Computer Science and Engineering Dept., 2004.
  6. S. Z. Wang and H. J. Lee, "A cascade framework for a real-time statistical plate recognition system," IEEE Trans. Inform. Forensics Security, Vol.2, No.2, pp.267-282, Jun., 2007. https://doi.org/10.1109/TIFS.2007.897251
  7. F. Shafait, D. Keysers, and T. M. Breuel, "Efficient Implementation of Local Adaptive Thresholding Techniques Using Integral Images," SPIE, Document Recognition and Retrieval XV, Jan., 2008.
  8. B.-F. Wu, S.-P. Lin, and C.-C. Chiu, "Extracting characters from real vehicle license plates out-of-doors," IET Comput. Vision, Vol.1, No.1, pp.2-10, 2007. https://doi.org/10.1049/iet-cvi:20050132
  9. T. D. Duan, T. L. H. Du, T. V. Phuoc, and N. V. Hoang, "Building an automatic vehicle license-plate recognition system," Proc. Int. Conf. Comput. Sci. RIVF, pp.59-63, 2005.
  10. Q. Gao, X. Wang, and G. Xie, "License plate recognition based on prior knowledge," Proc. IEEE Int. Conf. Automat. Logistics, pp.2964-2968, Aug., 2007.
  11. C. A. Rahman, W. Badawy, and A. Radmanesh, "A real time vehicle's license plate recognition system," Proc. IEEE Conf. Adv. Video Signal Based Surveillance, pp.163-166, Jul., 2003.
  12. I. S. Choi, M. Y. Jin, D. S. Park, "Vechicle License Plate Detection and Optical Number Recogniton Using Morphological processing and Neural Network", Proc. KISM Spring Conference, Vol.2, No.1, pp.58-61, May, 2013.
  13. K. K. Kim, K. I. Kim, J. B. Kim, and H. J. Kim, "Learning-based approach for license plate recognition," Proc. IEEE Signal Process. Soc. Workshop Neur. Netw. Signal Process., Vol.2, pp.614-623, Dec., 2000.
  14. D. Llorens, A. Marzal, V. Palazon, and J. M. Vilar, "Car license plates extraction and recognition based on connected components analysis and HMM decoding," Lecture Notes Comput. Sci., Vol.3522, pp.571-578, 2005.
  15. V. Kamat and S. Ganesan, "An efficient implementation of the Hough transform for detecting vehicle license plates using DSPs," Proc. Real-Time Tech. Applicat. Symp., pp.58-59, 1995.
  16. Shan Du, Mahmoud Ibrahim, Mohamed Shehata and Wael Badawy, "Automatic License Plate Recognition (ALPR): A State-of-the-Art Review," IEEE Transactions on Circuits and Systems for Video Technology, Vol.23, No.2, pp.311-325, Feb., 2013. https://doi.org/10.1109/TCSVT.2012.2203741
  17. Shan Du, Mahmoud Ibrahim, Mohamed Shehata, and Wael Badawy, "Automatic License Plate Recognition (ALPR) : A State-of-the-Art Review," IEEE Transactions on Circuits and Systems for Video Technology, Vol.23, No.2, pp.311-325, Feb., 2013. https://doi.org/10.1109/TCSVT.2012.2203741
  18. Zuiderveld, Karel, "Contrast Limited Adaptive Histograph Equalization," Graphic Gems IV. San Diego: Academic Press Professional, pp.474-485, 1994.
  19. C. Tomasi, R. Manduchi, "Bilateral Filtering for Gray and Color Images," Proc. Int. Conf. Computer Vision (ICCV), 1998.
  20. N. Otsu, "A threshold selection method from gray-level histograms," IEEE Trans. Systems, Man, and Cybernetics Vol.9, No.1, pp.62-66, 1979. https://doi.org/10.1109/TSMC.1979.4310076
  21. Y. Yoon, K. Ban, H. Yoon, J. Lee, and J. Kim, "Best Combination of Binarization Methods for License Plate Character Segmentation," ETRI Journal, Vol.35, No.3, pp.491-500, Jun., 2013. https://doi.org/10.4218/etrij.13.0112.0545
  22. R. Achanta, S. Hemami, F. Estrada, and S. Susstrunk, "Frequency-tuned salient region detection," IEEE Conference on Computer Vision and Pattern Recognition, 2009.

Cited by

  1. A Study on Grapheme and Grapheme Recognition Using Connected Components Grapheme for Machine-Printed Korean Character Recognition vol.21, pp.9, 2016, https://doi.org/10.9708/jksci.2016.21.9.027