한국정보과학회논문지:컴퓨팅의 실제 및 레터 (Journal of KIISE:Computing Practices and Letters)

한국정보과학회 (Korean Institute of Information Scientists and Engineers)
권호 표지

다중 차량 연관 추적을 위한 겹침 제거 및 배경영상 갱신

Overlap Removal and Background Updating for Associative Tracking of Multiple Vehicles

  • 임준식 (전남대학교 전자컴퓨터공학과) ;
  • 김수형 (전남대학교 전자컴퓨터공학과) ;
  • 이칠우 (전남대학교 전자컴퓨터공학과) ;
  • 이명은 (전남대학교 전자컴퓨터공학부)
  • 발행 : 2010.01.15
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

본 논문에서는 지능형 교통정보 시스템에서 활용할 수 있는 차량의 연관 추적 방법에 관하여 제안한다. 차량의 연관 추적과정에서 발생하는 차량 간 겹침 문제를 해결하기 위하여 위치 평균값과 시공간 연관 정보를 이용한 연관 추적 방법을 제안하였고 배경영상의 신뢰도를 향상시키기 위하여 배경영상 갱신 방법을 제안하였다. 제안한 방법의 성능 평가를 위하여 다양한 위치의 교통 정보 수집 CCTV에서 촬영된 영상을 사용하였고 평균 96% 이상의 추적 성공률을 보였다.

In this paper, we propose a vehicle tracking method that can be applied in the intelligent traffic information system. The proposed method mainly consists of two steps: overlap removal and background updating. In order to remove overlap, we detect the overlap based on the location of the vehicle from successive images. Background updating is to calculate a background using statistical analysis of successive images. We collected a set of test images from the traffic monitoring system and experimented. The experimental results show more than 96% of tracking accuracy.

키워드

참고문헌

  1. A. Yilmaz, O. Javed and M. Shah, "Object Tracking: A Survey," ACM Computing Surveys, vol.38, Issue 4, no.13, Dec. 2006.
  2. J. Zhou, D. Gao, D. Zhang, "Moving Vehicle Detection for Automatic Traffic Monitoring," IEEE Transactions on Vehicular Technology, vol.56, no.1, pp.51-59, 2007. https://doi.org/10.1109/TVT.2006.883735
  3. N. Jacobs, R. Pless, "Time Scales in Video Surveillance," IEEE Transactions on Circuits and Systems for Video Technology, vol.18, no.8, Aug. 2008.
  4. S. Cheng, X. Luo, and S. M. Bhandarkar, "A Multiscale Parametric Background Model for Stationary Foreground Object Detection," In Proc. IEEE Workshop on Motion and Video Computing (WMCV), pp.18, Feb. 2007.
  5. S. Cheng, X. Luo, and S. M. Bhandarkar, "A Multiscale Parametric Background Model for Stationary Foreground Object Detection," In Proc. IEEE Workshop on Motion and Video Computing (WMCV), pp.18, Feb. 2007.
  6. K. Garg and S. Nayar. "Vision and rain," International Journal of Computer Vision", vol.75, no.1, pp.3-27, Oct. 2007. https://doi.org/10.1007/s11263-006-0028-6
  7. T. Alexandropoulos, S. Boutas, V. Loumos, E. Kayafas, "Real-time change detection for surveillance in public transportation," IEEE Conf. on Advanced Video and Signal Based Surveillance, pp.58-63, 2005.
  8. N. M. Oliver, B. Rosario and A. P. Pentland, "A Bayesian Computer Vision System for Modeling Human Interactions," IEEE Transactions PAMI, vol.22, no.8, pp.831-843, Aug. 2000. https://doi.org/10.1109/34.868684
  9. R. C. Gonzalez and R. E. Woods, Digital Image Processing, Addison Wesley, pp.458-465, 1992.
  10. J. I. Agbinya and D. Rees, "Multi-object tracking in video," Real Time Imaging, vol.5, Issue 5, pp.295-304, 1999. https://doi.org/10.1006/rtim.1998.0174
  11. D. Comaniciu and P. Meer, "Mean shift: a Robust Approach Toward Feature Space Analysis," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.24, pp.603-619, 2002. https://doi.org/10.1109/34.1000236
  12. D. Roller, J. Daniilidis and H. Nagel, "Model-based Object Tracking in Monocular Image Sequences of Road Traffic Scenes," International Journal of Computer Vision, vol.10, no.3, pp.257-281, 1993. https://doi.org/10.1007/BF01539538
  13. E. C. Kim, S. H. Kim, G. S. Lee and H. J. Yang, "Real-Time Traffic Information Collection Using Multiple Virtual Detection Lines," Journal of KIPS, vol.15B, no.6, pp.543-552, Dec. 2008.