Journal of KIISE:Computer Systems and Theory (한국정보과학회논문지:시스템및이론)

Korean Institute of Information Scientists and Engineers (한국정보과학회)
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Vision Based Vehicle Detection and Traffic Parameter Extraction

비젼 기반 차량 검출 및 교통 파라미터 추출

  • 하동문 (아주대학교 컴퓨터네트워크 연구실) ;
  • 이종민 (아주대학교 교통연구소) ;
  • 김용득 (아주대학교 전자공학부)
  • Published : 2003.12.01
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Abstract

Various shadows are one of main factors that cause errors in vision based vehicle detection. In this paper, two simple methods, land mark based method and BS & Edge method, are proposed for vehicle detection and shadow rejection. In the experiments, the accuracy of vehicle detection is higher than 96%, during which the shadows arisen from roadside buildings grew considerably. Based on these two methods, vehicle counting, tracking, classification, and speed estimation are achieved so that real-time traffic parameters concerning traffic flow can be extracted to describe the load of each lane.

다양한 그림자는 비젼 기반 차량 검출에서 오류를 발생시키는 주요 원인이다. 본 논문에서는 노면 표시 기반 방법과 배경 빼기 및 에지(BS & Edge) 방법이라는 두 가지 방안을 차량 검출과 그림자 제거를 위해 제안하였다. 노변의 지형 지물들로 인해서 발생하는 그림자의 영향이 크게 증가하는 상황에서의 실험을 통해서 96% 이상의 차량 검출 정확도를 나타냄을 확인하였다. 전술한 두 가지 방법을 기반으로 하여, 차량 추적, 차량 계수, 차종 분류, 그리고 속도 측정을 수행하여 각 차로의 부하를 나타내는 데 사용되는 차량 흐름과 관련된 여러 가지 교통 파라미터를 추출하였다.

Keywords

References

  1. Klein Lawrence A., Kelley Michael R., and Mills Milton K., Evaluation of overhead and in-ground vehicle detector technologies for traffic flow measurement, Journal of Testing & Evaluation, vol.25, no.2, pp.205-214, 1997 https://doi.org/10.1520/JTE11480J
  2. A .Rouke and M. G. H. Bell, Traffic analysis using low cost image processing, in Proc. Seminar on Transportation Planning Methods, PTRC, Bath,U.K., 1988
  3. N. Hoose, Computer Image Processing in Traffic Engineering, U.K.:Taunton Research Studies Press, 1991
  4. K. W. Dickinson and R. C. Waterfall, Image processing applied to traffic: A general review, Traffic Eng. Contr., vol. 1, pp.6-13, Jan. 1984
  5. Image Processing applied to traffic: Practical experience, Traffic Eng. Contr., vol. 2, pp.60-67, Feb. 1984
  6. N. Hashimoto et al., Development of an image processing traffic flow measurement system, Sumitomo Electronic Tech. Rev., no 25, pp.133-138, Jan. 1988
  7. S. Takaba et al., A traffic flow measuring system using a solid state sensor, in Proc. IEE Conf. Road Traffic Data Collection, London,U.K., 1984
  8. M. Fathy and M. Y. Siyal, A window-based image processing technique for quantiative and qualitative analysis of road traffic parameters, IEEE Trans. on Vehicular Technology, vol.47, no.4, pp.1342-1349, 1998 https://doi.org/10.1109/25.728525
  9. M. Fathy and M.Y, Siyal, An image detection technique based on morphological edge detection and background differencing for real time traffic analysis, Pattern Recognition Lett., vol. 16, pp.1321-1330, 1995 https://doi.org/10.1016/0167-8655(95)00081-X
  10. M. Fathy and M. Y. Siya, A Window-based edge detection technique for measuring road traffic parameters in real-time, Real-Time Imaging 1, 1995, pp.297-305 https://doi.org/10.1006/rtim.1995.1028
  11. Hongjiang Zhang, Yihong Gong, Dan Patterson, and Atreyi Kankanhalli, Moving object detection, tracking and recognition, The Third International Conference on Automation, Robotics and Computer Vision, pp.1990-1994, 1994
  12. P.G. Michalopulos, Vehicle detection video through image processing: The Autoscope system, IEEE Trans. on Vehicular Technology, vol.40, no.1, pp.21-29, 1991 https://doi.org/10.1109/25.69968
  13. Yang Ju Kim, and Young Sung Soh, Improvement of background update method for image detector, 5th World Congress on Intelligent Transport Systems, CD-version, 1998
  14. Jiang Zifeng, A shadow rejection algorithm for vehicle presence detection Conference on Intelligent Vehicles, 1998 IEEE International Conference on Intelligent Vehicles, pp.182-188, 1998