A Vehicle Detection System Robust to Environmental Changes for Preventing Crime

환경 변화에 강인한 방범용 차량 검지 시스템

  • Received : 2010.03.05
  • Accepted : 2010.03.31
  • Published : 2010.07.31

Abstract

The image processing technique is very sensitive to the variation of external environment, so it tends to lose a lot of accuracy when the external environment changes rapidly. In this paper, we propose a vehicle detecting and tracking system for crime prevention suitable for an external environments with various changes using the image processing technique. Because the vehicle camera detector for crime prevention extracts and tracks the vehicle within one lane, it is important to classify a characteristic region rather than the contour of a vehicle. The proposed system detects the entrance of the vehicle using optical flow and tracks the vehicle by classifying the headlights, the bonnet, the front-window and the roof area of the vehicle. Experimental results show that the proposed method is robust to the environmental changes such as type, speed and time of a vehicle.

외부 환경에서의 영상처리 기술은 환경에 매우 민감하여 외부환경이 급격하게 변화할 때마다 정확도가 많이 떨어지는 경향이 있다. 본 논문에서는 다양한 변화가 일어나는 실외환경에서 영상처리 기술을 이용한 방범용 차량 검지 및 추적 시스템을 제안한다. 방범용 카메라검지기는 하나의 차선내에서 차량을 검지하고 추적하기 때문에 차량의 윤곽보다는 차량의 특징 영역을 분리하는 것이 중요하다. 제안한 시스템은 차량 진입의 판단을 광류를 통하여 검지하며, 차량의 전조등, 본넷, 전면창, 루프 등으로 영역을 분류하여 차량을 추적한다. 실험을 통하여 제안한 시스템이 차량의 종류, 속도 및 시간 의 환경 변화에도 강인함을 확인하였다.

Keywords

References

  1. Y. Park, "Shape-resolving local thresholding for object detection." Pattern Recognition Letters, Vol.22, No.8, pp. 883-890, 2001. https://doi.org/10.1016/S0167-8655(01)00034-4
  2. S. Gupte, et al., "Detection and classification of vehicles," IEEE Transactions on Intelligent Transportation Systems, Vol.3, No.1, pp. 37-47, 2002. https://doi.org/10.1109/6979.994794
  3. Y. Jung and Y. Ho, "Traffic parameter extraction using video-based vehicle tracking," Proceedings of IEEE International Conference on ITS, pp. 764-766, 1999.
  4. B. Maurin, et al., "Monitoring crowded traffic scenes," Proceedings of IEEE 5th International Conference on Intelligent Transportation Systems, pp. 19-24, 2002.
  5. L.D. Stefano and E. Viarani, "Vehicle detection and tracking using the block matching algorithm," Proceeding of 3rd IMACE/ IEEE, Vol.1, pp. 4491-4496, 1999.
  6. R. Cucchiara, M. Piccardi, and P. Mello, "Image analysis and rule-based reasoning for a traffic monitoring System," IEEE Transactions on Intelligent Transportation Systems, Vol. 1, No. 2, pp. 119-130, 2000. https://doi.org/10.1109/6979.880969
  7. Andrew H.S. Lai and Nelson H.C. Yung, "Vehicle-type identification through automated virtual loop assignment and block-based direction-biased motion estimation," IEEE Transactions on Intelligent Transportation Systems, Vol. 1, No. 2, pp. 86-97, 2000. https://doi.org/10.1109/6979.880965
  8. 최영진, 양해술, "화상처리 기술을 이용한 자동차 교통제어에 관한 논문," 한국정보처리응용 학회 논문지, 제1권, 제3호 1994.09.
  9. Dar-Shyang Lee, "Effective gaussian mixture learning for video background subtraction," IEEE Transaction on Pattern Analysis and Machine Intelligence, Vol.27, No.5, pp. 827-832, 2005. https://doi.org/10.1109/TPAMI.2005.102
  10. C.Harris and M.Stephens, "A combined corner and edge detector," Proceedings of the 4th Alvey Vision Conference, pp. 147-151, 1988.
  11. J. Shi and C. Tomasi, "Good features to track," 9th IEEE Conference on Coumputer Vision and Pattern Recognition, June 1994.
  12. G. R. Bradski and A. Kaehler, Learning OpenCV 제대로 배우기, 한빛미디어, 2009.
  13. B.K.P. Horn and B.G. Schunk, "Determining optical flow," Artificial Intelligence 17, pp. 185-203, 1981.
  14. Subbarao, M. Waxman and A.M., "Closed form solutions to image flow equations for planar surfaces in motion," CVGIP(36), pp. 208-228, 1986.
  15. B.D. Lucas and T.Kanade, "An interative image registration technique with an application to stereo vision," Proceedinsgs of the 1981 DARPA Imaging Understanding Workshop, pp. 121-130, 1981.
  16. M. Sonka, V. Hlavac, and R. Boyle, Image Processing Analysis, and Machine Vision, PWS Publishing, 1999.