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

  • 제37권7C호
  • /
  • Pages.554-564
  • /
  • 2012
  • /
  • 1226-4717(pISSN)
  • /
  • 2287-3880(eISSN)
한국통신학회 (The Korean Institute of Commucations and Information Sciences)
권호 표지
DOI QR코드

DOI QR Code

도심 영상에서의 비음수행렬분해를 이용한 차량 인식

Vehicle Recognition using NMF in Urban Scene

  • 반재민 (인천대학교 정보통신공학과 영상 연구실) ;
  • 이병래 (한국방송통신대학교 컴퓨터과학과) ;
  • 강현철 (인천대학교 정보통신공학과)
  • 투고 : 2012.04.30
  • 심사 : 2012.07.02
  • 발행 : 2012.07.31
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

차량인식은 차량 후보영역 검출단계와 검출된 후보 영역에서 특징을 기반으로 차량을 검증하는 차량 검증단계로 나누어진다. 선형 변환 방식의 특징은 차원 감소 효과와 통계적인 특징을 지니게 되어, 이동이나 회전에 강인한 특성을 갖는다. 선형 변환 방식 중 비음수행렬분해(Non-negative Matrix Factorization, NMF)는 부분 기반 표현 방식으로 차량의 국소적인 특징을 기저벡터로 사용하여 희소성을 갖는 특징을 추출할 수 있기 때문에 도심영상에서 발생하는 차폐 영역에 따른 인식률 저하를 방지할 수 있다. 본 논문에서는 차량 인식에 적합한 NMF 특징 추출 방법을 제안하고, 인식률을 검증하였다. 또한 희소성 제약 조건을 이용하여 기저 벡터에 희소성을 가지는 SNMF(Sparse NMF)와 LVQ2(Learning Vector Quantization) 신경 회로망을 결합하여 차폐 영역에 대한 차량 인식 효율을 기존의 NMF를 이용한 방법과 비교하였다. NMF를 이용하는 특징은 차량이 혼재되어 차폐 영역이 빈번히 발생하는 도심에서도 강건한 특징임을 보였다.

The vehicle recognition consists of two steps; the vehicle region detection step and the vehicle identification step based on the feature extracted from the detected region. Features using linear transformations have the effect of dimension reduction as well as represent statistical characteristics, and show the robustness in translation and rotation of objects. Among the linear transformations, the NMF(Non-negative Matrix Factorization) is one of part-based representation. Therefore, we can extract NMF features with sparsity and improve the vehicle recognition rate by the representation of local features of a car as a basis vector. In this paper, we propose a feature extraction using NMF suitable for the vehicle recognition, and verify the recognition rate with it. Also, we compared the vehicle recognition rate for the occluded area using the SNMF(sparse NMF) which has basis vectors with constraint and LVQ2 neural network. We showed that the feature through the proposed NMF is robust in the urban scene where occlusions are frequently occur.

키워드

참고문헌

  1. D. D. Lee and H. S. Seung, "Learning the parts of objects by non-negative matrix factorizatio n," Nature, vol.401, pp.788-791, 1999. https://doi.org/10.1038/44565
  2. Z. Sun, "On-Road Vehicle Detection: A Review," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 28, no 9, pp. 694-711, 2006. https://doi.org/10.1109/TPAMI.2006.104
  3. A. Kuehnle, "Symmetry-Based Recognition for Vehicle Rears," Pattern Recognition Letters, vol. 12, pp. 249-258, 1991. https://doi.org/10.1016/0167-8655(91)90039-O
  4. S.D. Buluswar and B.A. Draper, "Color Machine Vision for Autonomous Vehicles," Int'l J. Eng. Applications of Artificial Intelligence, vol. 1, no. 2, pp. 245-256, 1998.
  5. C. Tzomakas and W. Seelen, "Vehicle Detection in Traffic Scenes Using Shadows," Technical Report 98-06, Institut fur Neuro informatik, Ruht Universitat, Bochum, Germany, 1998.
  6. M. Bertozzi, A. Broggi, and S. Castelluccio, "A Real-Time Oriented System for Vehicle Detection," J. Systems Architecture, pp. 317- 325, 1997.
  7. N. Srinivasa, "A Vision-Based Vehicle Detection and Tracking Method for Forward Collision Warning," Proc. IEEE Intelligent Vehicle Symp., pp. 626-631, 2002.
  8. A. Giachetti, M. Campani, and V. Torre, "The Use of Optical Flow for Road Navigation," IEEE Trans. Robotics and Automation, vol. 14, no. 1, pp. 34-48, 1998. https://doi.org/10.1109/70.660838
  9. R. Mandelbaum, L. McDowell, L. Bogoni, B. Beich, and M.Hansen, "Real-Time Stereo Processing, Obstacle Detection, and Terrain Estimation from Vehicle-Mounted Stereo Cameras," Proc. IEEE Workshop Applications of Computer Vision, pp. 288-289, 1998.
  10. U. Franke and I. Kutzbach, "Fast Stereo Based Object Detection for Stop and Go Traffic," Intelligent Vehicles, pp. 339-344, 1996.
  11. U. Regensburger and V.Graefe, "Visual Recognition of Obstacles on Roads," Intelligent Robots and Systems, pp. 73-86, 1995.
  12. T. Kalinke, C. Tzomakas, and W. von Seelen, "A Texture-Based Object Detection and an Adaptive Model-Based Classification," Proc. IEEE Int'l Conf. Intelligent Vehicles, pp. 143-148, 1998.
  13. Z. Sun, G. Bebis, and R. Miller, "On-Road Vehicle Detection Using Gabor Filters and Support Vector Machines," Proc. IEEE Int'l Conf' Digital Signal Processing, July 2002.
  14. J. Wu and X. Zhang, "A PCA Classifier and Its Application in Vehicle Detection," Proc. IEEE Int'l Joint Conf. Neural Networks, 2001.
  15. S. Mika, "Fisher discriminant analysis with kernels," IEEE Conference on Neural Networks for Signal Processing IX, pp. 41-48, 1999.
  16. A. Hyvarinen, "Fast and Robust Fixed-Point Al gorithm for Independent Component Analysis," IEEE Trans. Neural Networks, vol.10, no 3, pp. 626-634, 1999. https://doi.org/10.1109/72.761722
  17. N.Naveen, "Parts based representation for pedestrian using NMF with robustness to partial occlusion,"Signal Processing and Communica tions (SPCOM), 2010 International Conference., 2010.
  18. P. O. Hoyer, "Non-negative matrix factorization with sparseness constraints," The Journal of Machine Learning Research, vol. 5, pp. 1457- 1469, 2004.
  19. T. Kohonen, J. Hunninen, J. Kangas, J. Kaaaksonen, and K. Torkkola, "LVQ_Pak : The Learning Vector Quantization Program Package," Technical Report A30, Helsinki Univ. 1996.
  20. C. Wu, L. Duan, J. Miao, F. Fang, and X. Wang. "Detection of Front-view Vehicle with Occlusions Using AdaBoost," International Conference on Information Engineering and Computer Science, 2009. ICIECS 2009., 2009.
  21. KITTI Vision Benchmark, http://www.cvlibs.net/,2010