Moving Object Detection using Gaussian Pyramid based Subtraction Images in Road Video Sequences

가우시안 피라미드 기반 차영상을 이용한 도로영상에서의 이동물체검출

  • Received : 2011.11.01
  • Accepted : 2011.12.13
  • Published : 2011.12.31


In this paper, we propose a moving object detection method in road video sequences acquired from a stationary camera. Our proposed method is based on the background subtraction method using Gaussian pyramids in both the background images and input video frames. It is more effective than pixel based subtraction approaches to reduce false detections which come from the mis-registration between current frames and the background image. And to determine a threshold value automatically in subtracted images, we calculate the threshold value using Otsu's method in each frame and then apply a scalar Kalman filtering to the threshold value. Experimental results show that the proposed method effectively detects moving objects in road video images.


Moving Object Detection;Gaussian Pyramid;Road Video Sequences


  1. V. Kastrinaki, M. Zervakis, and K. Kalaitzakis, "A survey of video processing techniques for traffic applications," Image and Vision Computing, vol. 21, pp. 359-381, 2003.
  2. Guolin Wang et al, "Review on Vehicle Detection Based on Video for Traffic Surveillance," Proceedings of the IEEE International Conference on Automation and Logistics Qingdao, China September 2008.
  3. A. Murat Tekalp, Digital Video Processing, Prentice Hall PTR, 1995.
  4. Alan M. McIvor, "Background Subtraction Techniques," in Proc. of Image and Video Computing New Zealand, 2000.
  5. Massimo Piccardi, "Background subtraction techniques: a review," IEEE International Conference on Systems, Man and Cybernetics, pp.3099-3104, 2004.
  6. C. Wren, A. Azabayejani, T. Darrell and A. Pentland, "Pnder: Real-time tracking of the human body," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 19, no. 7, pp. 780-785, 1997.
  7. Jianyang Zheng et. al. "Extracting Roadway Background Image: a Mode-Based Approach," TRB 2006 Annual Meeting.
  8. J. Zheng, Y. Wang, N. Nihan, E. Hallenbeck, "Extracting Roadway Background Image: A mode based approach", TRB 2006, 2006.
  9. C. Ridder, O. Munkelt, and H. Kirchner, "Adaptive Background Estimation and Foreground Detection using Kalman-Filtering", Int. Conf. on Recent Advances in Mechatronics, UNESCO Chair on Mechatronics, 1995, pp. 193-199.
  10. C. Stauffer and E. Grimson, "Adaptive background mixture models for real-time tracking", IEEEComputerVision and Pattern Recognition Conference, Vol.2, pp. 246-252, 1998
  11. C. Stauffer, W.E.L. Grimson, "Learning patterns of activity using real-ime tracking", IEEE Trans. on Patt. Anal. and Machine Intell., vol. 22, no. 8, pp. 747-757, 2000
  12. R. Aarthi, S. Padmavathi, and J. Amudha, "Vehicle detection in static images using color and corner map," in ITC '10: Proceedings of the 2010 International Conference on Recent Trends in Information, Telecommunication and Computing. Washington, DC, USA: IEEE Computer Society, 2010, pp. 244--246.
  13. J. Zhou, D. Gao, D. Zhang, "Moving vehicle detection for automatic traffic monitoring," IEEE Trans on Vehicular Technology, 56, pp.51-59, 2007.
  14. Rensso et al, "Progressive Background Image Generation of Surveillance Traffic Videos Based on a Temporal Histogram Ruled by a Reward/Penalty Function," SIBGRAPI Conference on Graphics, Patterns and Images, 24, 2011.
  15. K. Song, J. Tai, "Real-Time Background Estimation of Traffic Imagery Using Group-Based Histogram", Journal of Information Science and Engineering, Volume 24, pages 411-423, 2008.
  16. S. Zhang, H. Yao, S. Liu, "Dynamic Background Subtraction Based on Local Dependency Histogram", International Journal of Pattern Recognition and Artificial Intelligence, IJPRAI 2009, 2009.
  17. Erhan A. Ince et. al, "Background Subtraction and Lane Occupancy Analysis, Video Surveillance, chap. 10, InTech, February 2011.
  18. Jose Manuel Milla et al, Computer Vision Techniques for Background Modeling in Urban Traffic Monitoring, Urban Transport and Hybrid Vehicles, InTech, August 2010.
  19. Sen-Ching S. Cheung and Chandrika Kamath, "Robust techniques for background subtraction in urban traffic video," Proceedings of Electronic Imaging: Visual Communications and Image Processing WA:SPIE. 5308, pp.881-892, 2004.
  20. Nobuyuki Otsu "A threshold selection method from gray-level histograms". IEEE Trans. Sys., Man., Cyber. 9 (1), pp.62-66, 1979.
  21. Welch and Bishop, "An Introduction to the Kalman Filter," SIGGRAPH 2001 Course8.


Supported by : 공주대학교