ADAPTIVE BACKGROUND SUBTRACTION IN H.264/AVC BITSTREAMS BASED ON MACROBLOCK SIZES

Antoine Vacavant, Lionel Robinault, Serge Miguet, Chris Poppe, Rik van de Walle

Abstract

In this article, we propose a novel approach to detect moving objects in H.264 compressed bitstreams. More precisely, we describe a multi-modal background subtraction technique that uses the size of macroblocks in order to label them as belonging to the background of the observed scene or not. Here, we integrate an adaptive Gaussian mixture-based scheme to model the background. We evaluate our contribution using the PETS video dataset and a realist synthetic video sequence rendered by a 3-D urban environment simulator. We compare two different background models, and we show that the Gaussian mixture-based is the best and outperforms other techniques that use macro bloc sizes.

References

  1. Brown, L. M., Senior, A. W., Tian, Y., Connell, J., Hampapur, A., Shu, C., Merkl, H., and Lu, M. (2005). Performance evaluation of surveillance systems under varying conditions. In IEEE International Workshop on Performance Evaluation of Tracking and Surveillance (PETS). http://www.research.ibm.com/people vision/performanceevaluation.html.
  2. Chen, Y.-T., Chen, C.-S., Huang, C.-R., and Hung, Y.-P. (2007). Efficient hierarchical method for background subtraction. Pattern Recognition, 40(10):2706-2715.
  3. Cheung, S. S. and Kamath, C. (2004). Robust techniques for background subtraction in urban traffic video. In Proceedings of SPIE, volume 5308, pages 881-892.
  4. De Bruyne, S., Poppe, C., Verstockt, S., Lambert, P., and Van de Walle, R. (2009). Estimating motion reliability to improve moving object detection in the h.264/avc domain. In IEEE International Conference on Multimedia and Expo (ICME), pages 330-333.
  5. Dhome, Y., Tronson, N., Vacavant, A., Chateau, T., Gabard, C., Goyat, Y., and Gruyer, D. (2010). A benchmark for background subtraction algorithms in monocular vision: a comparative study. In IEEE International Conference on Image Processing Tools, Theory and Applications (IPTA). To appear.
  6. Goyat, Y., Chateau, T., Malaterre, L., and Trassoudaine, L. (2006). Vehicle trajectories evaluation by static video sensors. In IEEE Intelligent Transportation Systems Conference (ITSC), pages 864-869.
  7. Gruyer, D., Royere, C., du Lac, N., Michel, G., and Blosseville, J.-M. (2006). SiVIC and RTMaps, interconnected platforms for the conception and the evaluation of driving assistance systems. In World Congress and Exhibition on Intelligent Transport Systems and Services (ITSC), pages 1-8.
  8. Hayman, E. and Eklundh, J.-O. (2003). Statistical background subtraction for amobile observer. In IEEE International Conference on Computer Vision (CVPR).
  9. Kaewtrakulpong, P. and Bowden, R. (2001). An improved adaptive background mixture model for realtime tracking with shadow detection. In European Workshop on Advanced Video Based Surveillance Systems (AVSS).
  10. Kim, K., Chalidabhongse, T., Harwood, D., and Davis, L. (2005). Real-time Foreground-Background Segmentation using Codebook Model. Real-time Imaging, 11(3):167-256.
  11. Liu, Z., Lu, Y., and Zhang, Z. (2007). Real-time spatiotemporal segmentation of video objects in the H.264 compressed domain. Journal of Visual Communication and Image Representation, 18(3):275-290.
  12. Mehmood, K., Mrak, M., Calic, J., and Kondoz, A. (2009). Object tracking in surveillance videos using compressed domain features from scalable bit-streams. Image Communication, 24(10):814-824.
  13. Poppe, C., De Bruyne, S., Paridaens, T., Lambert, P., and Van de Walle, R. (2009). Moving object detection in the H.264/AVC compressed domain for video surveillance applications. Journal of Visual Communication and Image Representation, 20(6):428-437.
  14. Poppe, C., Martens, G., Lambert, P., and Van de Walle, R. (2007). Improved background mixture models for video surveillance applications. In Yagi, Y., Kang, S., Kweon, I., and Zha, H., editors, ACCV 2007, volume 4843 of LNCS, pages 251-260. Springer.
  15. Sigari, M. H. and Fathy, M. (2008). Real-time background modeling/subtraction using two-layer codebook model. In International MultiConference of Engineers and Computer Scientists.
  16. Solana-Cipres, C., Fernandez-Escribano, G., RodriguezBenitez, L., Moreno-Garcia, J., and Jimenez-Linares, L. (2009). Real-time moving object segmentation in H.264 compressed domain based on approximate reasoning. International Journal of Approximate Reasoning, 51(1):99-114.
  17. Stauffer, C. and Grimson, W. E. L. (1999). Adaptative background mixture models for a real-time tracking. In IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), volume 2, pages 246-252.
  18. Tuzel, O., Porikli, F., and Meer, P. (2005). A bayesian approach to background modeling. In Conference on Computer Vision and Pattern Recognition (CVPR).
  19. Wiegand, T., Sullivan, G., Bjontegaard, G., and Luthra, G. (2003). Overview of the H.264/AVC video coding standard. IEEE Transactions on Circuits and Systems for Video Technology, 13(7):560-576.
  20. XiaHou, X.-J. and Gong, S.-R. (2008). Adaptive shadows detection algorithm based on Gaussian mixture model. In International Symposium on Information Science and Engineering.
  21. You, W., Houari Sabirin, M. S., and Munchurl, K. (2007). Moving object tracking in H.264/AVC bitstream. In Multimedia Content Analysis and Mining (MCAM), volume 4577 of LNCS, pages 483-492. Springer.
  22. Zivkovic, Z. (2004). Improved adaptive gaussian mixture model for background subtraction. In IEEE International Conference on Pattern Recognition (ICPR).
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Paper Citation


in Harvard Style

Vacavant A., Robinault L., Miguet S., Poppe C. and van de Walle R. (2011). ADAPTIVE BACKGROUND SUBTRACTION IN H.264/AVC BITSTREAMS BASED ON MACROBLOCK SIZES . In Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2011) ISBN 978-989-8425-47-8, pages 51-58. DOI: 10.5220/0003364000510058


in Bibtex Style

@conference{visapp11,
author={Antoine Vacavant and Lionel Robinault and Serge Miguet and Chris Poppe and Rik van de Walle},
title={ADAPTIVE BACKGROUND SUBTRACTION IN H.264/AVC BITSTREAMS BASED ON MACROBLOCK SIZES},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2011)},
year={2011},
pages={51-58},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003364000510058},
isbn={978-989-8425-47-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2011)
TI - ADAPTIVE BACKGROUND SUBTRACTION IN H.264/AVC BITSTREAMS BASED ON MACROBLOCK SIZES
SN - 978-989-8425-47-8
AU - Vacavant A.
AU - Robinault L.
AU - Miguet S.
AU - Poppe C.
AU - van de Walle R.
PY - 2011
SP - 51
EP - 58
DO - 10.5220/0003364000510058