한국통신학회논문지 (The Journal of Korean Institute of Communications and Information Sciences)

  • 제37권8A호
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  • Pages.639-647
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  • 2012
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  • 1226-4717(pISSN)
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  • 2287-3880(eISSN)
한국통신학회 (The Korean Institute of Commucations and Information Sciences)
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축소 다변수 다항식 분류기를 이용한 고속 차량 검출 방법

Fast On-Road Vehicle Detection Using Reduced Multivariate Polynomial Classifier

  • 김중락 (연세대학교 전기전자공학과 영상인식 연구실) ;
  • 유선진 (연세대학교 전기전자공학과 영상인식 연구실) ;
  • ;
  • 김도훈 (전자부품 연구원 무선플랫폼센터) ;
  • 이상윤 (연세대학교 전기전자공학과 영상인식 연구실)
  • 투고 : 2012.05.29
  • 심사 : 2012.08.16
  • 발행 : 2012.08.31
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초록

비전 기반의 차량 검출 기술은 자동 주행 보조 시스템에 있어서 가장 중요한 기술 중의 하나이다. 하지만 자동차 외형의 다양성 및 주변 환경의 변화로 인하여 정확하고 신뢰성 있는 차량 검출 시스템의 개발은 여전히 해결해야 될 문제로 남아 있다. 일반적으로 차량 검출 시스템은 두 단계로 구분할 수 있다. 차량 후보 영역을 검출하는 가설 생성(Hypothesis Generation(HG)) 단계와 가설 생성 단계에서 검출된 영역을 검증하는 가설 검증(Hypothesis Verification(HV)) 단계이다. 차량 검출은 HV 단계에서 최종적으로 검증 및 결정되기 때문에, HV 단계의 성능에 의하여 차량 검출의 성능이 결정되게 된다. 따라서, 본 논문에서는 축소 다변수 다항식 분류기(reduced multivariate polynomial pattern classifier(RM))를 HV 단계에 이용하여 고속 차량 검출 시스템을 구성하였다. 실험 결과 RM 분류기가 SVM 분류기 기반의 차량 검출 시스템보다 처리 속도 측면에서 월등한 성능을 보여 실시간 처리 기반의 차량 검출 시스템에 적합하다.

Vision-based on-road vehicle detection is one of the key techniques in automotive driver assistance systems. However, due to the huge within-class variability in vehicle appearance and environmental changes, it remains a challenging task to develop an accurate and reliable detection system. In general, a vehicle detection system consists of two steps. The candidate locations of vehicles are found in the Hypothesis Generation (HG) step, and the detected locations in the HG step are verified in the Hypothesis Verification (HV) step. Since the final decision is made in the HV step, the HV step is crucial for accurate detection. In this paper, we propose using a reduced multivariate polynomial pattern classifier (RM) for the HV step. Our experimental results show that the RM classifier outperforms the well-known Support Vector Machine (SVM) classifier, particularly in terms of the fast decision speed, which is suitable for real-time implementation.

키워드

참고문헌

  1. Z. Sun, G. Bebis and R. Miller, "On-road vehicle detection: a review," IEEE Trans. Pattern Anal. Mach. Intell., 28(5), pp. 694-711, 2006. https://doi.org/10.1109/TPAMI.2006.104
  2. K.A. Toh, Q.L. Tran and D. Srinivasan, "Benchmarking a Reduced Multivariate Polynormial Pattern Classifier," IEEE Trans. Pattern Anal. Mach. Intell., 26(6), pp. 740-755, 2004. https://doi.org/10.1109/TPAMI.2004.3
  3. P. Viola and M. J. Jones, "Rapid Object Detection using a Boosted Cascade of Simple Features," in Proc. the IEEE conf. on Computer Vision and Pattern Recognition, 1(2), pp. 511-518, 2001.
  4. Caltech datasets are from http://www.robots.ox.ac.uk/-vgg/data/
  5. O. Nakayama, M. Shiohara, S Sasaki, T Takashima and D Ueno, "Robust Vehicle Detection under Poor Environmental Conditions for Rear and Side Surveillance," IEICE Transactions on Information and System, E87-D(1), pp. 97-104, 2004.
  6. A. Giachetti, M. Campani, and V. Torre, "The Use of Optical Flow for Road Navigation," IEEE Trans. Robotics and Automation, 14(1), pp. 34-48, 1998. https://doi.org/10.1109/70.660838
  7. Z. Sun, G. Bebis and R. Miller, "Improving the performance of on-road vehicle detection by combining Gabor and wavelet features," in Proc. the IEEE conf. on Intelligent Transportation Systems, pp. 130-135, 2002.
  8. N. Matthews, P. An, D. Charnley, and C. Harris, "Vehicle Detection and Recognition in Greyscale Imagery," Control Engineering Practice, 4(4), pp. 473-479, 1996. https://doi.org/10.1016/0967-0661(96)00028-7
  9. Ju-Hyun Cho and Seogn-Dae Kim, "Object detection using multi-resolution mosaic in image sequences," Signal Processing : Image Communication, 20(3), pp. 233-253, 2005. https://doi.org/10.1016/j.image.2004.12.001
  10. A. Kuehnle, "Symmetry-Based Recognition for Vehicle Rears," Pattern Recognition Letters, 12(4), pp. 249-258, 1991. https://doi.org/10.1016/0167-8655(91)90039-O
  11. J.C. Rojas and J.D. Crisman, "Vehicle detection in color images," in Proc. the IEEE conf. on Intelligent Transportation System, pp. 403-408, 1997.
  12. C. Tzomakas and W. Seelen, "Vehicle Detection in Traffic Scenes Using Shadows," Technical Report 98-06, Institut fur Neuroinformatik, Ruht-Universitat, Bochum, Germany, 1998.
  13. M. Bertozzi, A. Broggi, and S. Castelluccio, "A Real-Time Oriented System for Vehicle Detection," Journal of Systems Architecture, pp. 317-325, 1997.
  14. H. Mallot, H. Bulthoff, J. Little, and S. Bohrer, "Inverse Perspective Mapping Simplifies Optical Flow Computation and Obstacle Detection," Biological Cybernetics, 64(3), pp. 177-185, 1991. https://doi.org/10.1007/BF00201978
  15. R. Labayrade and D. Aubert, "In-vehicle obstacles detection and characterization by stereovision," in Proc. the 1st Workshop on In-Vehicle Cognitive Computer Vision Systems, 2003.
  16. A. Giachetti, M. Campani, and V. Torre, "The Use of Optical Flow for Road Navigation," IEEE Trans. Robotics and Automation, 14(1), pp. 34-48, 1998. https://doi.org/10.1109/70.660838
  17. A. Bensrhair, M. Bertozzi, A. Broggi, P. Miche, S. Mousset, and G. Moulminet, "A Cooperative Approach to Vision-Based Vehicle Detection," in Proc. the IEEE Conf. on Intelligent Transportation Systems, pp. 209-214, 2001.
  18. Z. Sun, R. Miller, G. Bebis and D. DiMeo, "A real-time precrash vehicle detection system", in Proc. IEEE Int'l Workshop Applications of Coputer Vision, pp. 171-176, 2002.
  19. S. Park, T. Kim, S. Kang, and K. Heon, "A Novel Signal Processing Technique for Vehicle Detection Radar," 2003 IEEE MTT-S Int'l Microwave Symp. Digest, pp. 607-610, 2003.
  20. Intel OpenCV library is from http://sourceforge.net/ projects/opencvlibrary/