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Improved Video Object Segmentation Method Based on Background Registration

In: 2012 International Conference on Information Technology and Management Science(ICITMS 2012) Proceedings

Author

Listed:
  • Wenxiu Fu

    (Beijing Jiaotong University)

  • Xingmin Wang

    (Beijing Jiaotong University)

  • Yuhang Wang

    (China University of Petroleum)

  • Bo Fan

    (Beijing Jiaotong University)

Abstract

In allusion to the common occlusion problems in video segmentation methods, a novel improved video object segmentation method based on background registration is proposed. At first, the high frequency message and most noise of the images were eliminated by 1-D wavelet transform. Then, calculate the front background modeling and the back background modeling by comparing the static index and the static threshold, and employ the secondary frame difference to optimize the front background modeling and the back background modeling, then the initializing background modeling can be obtained. Accumulated frame difference was used to optimize the initializing background modeling to reduce the probability of background that is misjudged in foreground, which makes further improvement on the segmentation results. Last pick-up the video object is obtained by mathematic morphologic method. Experimental results show that the background registration method was very non-sensitive to static threshold and has strong robustness. The algorithm can overcome effectively the occlusion and quickly giving accurate segmentation results from the sequence with static background for video object segmentation, it’s also can preferable the method in spatial accuracy.

Suggested Citation

  • Wenxiu Fu & Xingmin Wang & Yuhang Wang & Bo Fan, 2013. "Improved Video Object Segmentation Method Based on Background Registration," Springer Books, in: Bing Xu (ed.), 2012 International Conference on Information Technology and Management Science(ICITMS 2012) Proceedings, edition 127, pages 629-639, Springer.
    • Handle: RePEc:spr:sprchp:978-3-642-34910-2_72
    DOI: 10.1007/978-3-642-34910-2_72
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