DCT 계수를 이용한 속도 제한 표지판 인식 실시간 알고리듬의 설계

Design of a Real-time Algorithm for the Recognition of Speed Limit Signs Using DCT Coefficients

  • 강병휘 (서강대학교 전자공학과 CAD & ES 연구실) ;
  • 조한민 (서강대학교 전자공학과 CAD & ES 연구실) ;
  • 김재영 (한국전자통신연구원) ;
  • 황선영 (서강대학교 전자공학과 CAD & ES 연구실) ;
  • 김광수 (서강대학교 서강미래기술연구원)
  • 투고 : 2010.10.08
  • 심사 : 2010.11.26
  • 발행 : 2010.12.31

초록

본 논문은 지능형 자동차를 위한 속도 제한 표지판 실시간 인식 방법을 제안한다. 기존에는 전처리 과정을 거친 관심 영역에 대해 영역 전체의 픽셀 값을 특징으로 하여 연산량이 크나 제안된 방법은 연산량을 줄이기 위해 적은 개수의 DCT 계수를 이용하는 방법을 사용한다. 제안된 알고리듬은 인식의 판단 기준이 되는 DCT 계수를 선택하고 이를 선형판별법과 Mahalanobis Distance를 이용하여 단일 프레임의 속도 제한 표지판을 인식한다. 단일 프레임의 분류 결과를 연속된 프레임동안 누적하여 가장 높은 확률을 갖는 속도 제한 표지판을 선택한다. 실험 결과 테스트로 사용된 연속된 프레임에 대해서 100% 인식을 보이며 기존 대비, 곱셈 연산량은 58.6% 감소, 덧셈 연산량은 38.3% 감소하는 결과를 얻었다.

This paper proposes a real-time algorithm of recognizing speed limit signs for intelligent vehicles. Contrary to previous works which use all the pixel values in the ROI (Region Of Interest) after preprocessing image at ROI and need a lot of operations, the proposed algorithm uses fewer DCT coefficients in the ROI as features of each image to reduce the number of operations. Choosing a portion of DCT coefficients which satisfy discriminant criteria for recognition, the proposed algorithm recognizes the speed limit signs using the information obtained in the selected features through LDA and MD. It selects one having the highest probability among the recognition results calculated by accumulating the classification results of consecutive individual frames. Experimental results show that the recognition rate for consecutive frames reaches to 100% with test images. When compared with the previous algorithm, the numbers of multiply and add operations are reduced by 58.6% and 38.3%, respectively.

키워드

참고문헌

  1. 오준택, 곽현욱, 김욱현, "웨이블릿 변환과 형태 정보를 이용한 교통 표지판 인식", 대한전자공학회논문지, 41권 5호, pp.125-134, 2004년 9월.
  2. 김상환, 김진화, 김창수, "지능형 자동차를 위한 컴퓨터 비전 기술 동향", 대한전자공학회지, 37권 5호, pp.59-71, 2010년 5월.
  3. X. Baro, S. Escalera, J. Vitria, O. Pujol, and P. Radeva, "Traffic Sign Recognition Using Evolutionary Adaboost Detection and Forest-ECOC Classification", IEEE Trans. on Intelligent Transportation Systems, Vol.10, No.1, pp.113-126, Mar., 2009. https://doi.org/10.1109/TITS.2008.2011702
  4. C. Bahlmann, Y. Zhu, R. Visvanathan, M. Pellkofer, and T. Koehler, "A System for Traffic Sign Detection, Tracking, and Recognition Using Color, Shape, and Motion Information", in Proc. IEEE Intelligent Vehicles Symposium, Las Vegas, NV, pp.255- 260, June, 2005.
  5. C. Keller, C. Sprunk, C. Bahlmann, J. Giebel, and G. Baratoff, "Real-time Recognition of U.S. Speed Signs", in Proc. IEEE Intelligent Vehicles Symposium, Eindhoven, Netherland, pp.518-523, June, 2008.
  6. X. Gao, L. Podladchikova, D. Shaposhnikov, K. Hong, and N. Shevtsova, "Recognition of Traffic Signs Based on their Colour and Shape Features Extracted Using Human Vision Models", Journal of Visual communication and Image Representation, Vol.17, No.4, pp.675-685, Aug., 2006. https://doi.org/10.1016/j.jvcir.2005.10.003
  7. Y. Damavandi and K. Mohammadi, "Speed Limit Traffic Sign Detection and Recognition", in Proc. IEEE Cybernetics and Intelligent Systems, Singapore, Vol.2, pp.213-218, Dec., 2004.
  8. H. Shen and X. Tang, "Generic Sign Board Detection in Images", in Proc. ACM SIGMM international workshop on Multimedia information retrieval, Berkeley, CA, pp.144-149, Nov., 2003.
  9. B. Alefs, G. Eschemann, H. Ramoser, and C. Beleznai, "Road Sign Detection from Edge Orientation Histograms", in Proc. IEEE Intelligent Vehicles Symposium, Istanbul, Turkey, pp.993-998, June, 2007.
  10. D. Gavrila, "Traffic Sign Recognition Revisited", in Proc. DAGM-Symposium, Bonn, Germany, pp.86-93, Sep., 1999.
  11. P. Viola and M. Jones, "Robust Real-time Face Detection", International Journal of Computer Vision, Vol.57, No.2, pp.137-154, May, 2004. https://doi.org/10.1023/B:VISI.0000013087.49260.fb
  12. R. Vicen-Bueno, R. Gil-Pita, M. Rosa-Zurera, M. Utrilla-Manso, and F. Lopez-Ferreras, "Multilayer Perceptrons Applied to Traffic Sign Recognition Tasks", in Proc. International Work-Conference on Artificial Neural Networks, Barcelona, Spain, pp.865-872, June, 2005.
  13. S. Hsu and C. Huang, "Road Sign Detection and Recognition Using Matching Pursuit Method", Journal of Image and Vision Computing, Vol.19, No.3, pp.119-129, Feb., 2001. https://doi.org/10.1016/S0262-8856(00)00050-0
  14. P. Douville, "Real-Time Classification of Traffic Signs", Journal of Real-Time Imaging, Vol.6, No.3, pp.185-193, June, 2000. https://doi.org/10.1006/rtim.1998.0142
  15. Y. Aoyagi and T. Asakura, "A Study on Traffic Sign Recognition in Scene Image Using Genetic Algorithms and Neural Networks", in Proc. IEEE International Conference on Industrial Electronics, Control, and Instrumentation, Taipei, Vol.3, pp.1838-1843, Aug., 1996.
  16. S. Maldonado-Bascon, S. Lafuente-Arroyo, P. Gil-Jimenez, H. Gomez-Moreno, and F. Lopez -Ferreras, "Road-Sign Detection and Recognition Based on Support Vector Machines", IEEE Trans. on Intelligent Transportation Systems, Vol.8, No.2, pp.264-278, June, 2007. https://doi.org/10.1109/TITS.2007.895311
  17. A. de la Escalera, J. Armingol, J. Pastor, and F. Rodriguez, "Visual Sign Information Extraction and Identification by Deformable Models for Intelligent Vehicles", IEEE Trans. on Intelligent Transportation Systems, Vol.5, No.2, pp.57-68, June, 2004. https://doi.org/10.1109/TITS.2004.828173
  18. P. Paclik, J. Novovicova, P. Pudil, and P. Somol, "Road Sign Classification Using the Laplace Kernel Classifier", Pattern Recognition Letter, Vol.21, No.13/14, pp.1165-1173, Dec., 2000. https://doi.org/10.1016/S0167-8655(00)00078-7
  19. G. Hughes, "On the Mean Accuracy of Statistical Pattern Recognizers", IEEE Trans. on Information Theory, Vol.14, No.1, pp.55-63, Jan., 1968. https://doi.org/10.1109/TIT.1968.1054102
  20. A. Martinez and A. Kak, "PCA versus LDA", IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol.23, No.2, pp.228-233, Feb., 2001. https://doi.org/10.1109/34.908974
  21. Open Computer Vision Library, http://opencvlibrary.sourceforge.net.