Multimedia Tools and Applications

, Volume 76, Issue 8, pp 11003–11019 | Cite as

Local-to-global background modeling for moving object detection from non-static cameras

  • Aihua Zheng
  • Lei Zhang
  • Wei Zhang
  • Chenglong Li
  • Jin Tang
  • Bin Luo


This paper investigates efficient and robust moving object detection from non-static cameras. To tackle the motion of background caused by moving cameras and to alleviate the interference of noises, we propose a local-to-global background model for moving object detection. Firstly, motion compensation based local location-specific background model is deployed to roughly detect the foreground regions in non-static cameras. More specifically, the local background model is built for each pixel and represented by a set of pixel values drawn from its location and neighborhoods. Each pixel can be classified as foreground or background pixel according to the compensated background model based on the fast optical flow. Secondly, we estimate the global background model by the rough superpixel-based background regions to further separate foregrounds from background accurately. In particular, we use the superpixel to generate the initial background regions based on the detection results generated by local background model to alleviate the noises. Then, a Gaussian Mixture Model (GMM) is estimated for the backgrounds on superpixel level to refine the foreground regions. Extensive experiments on newly created dataset, including 10 challenging video sequences recorded in PTZ cameras and hand-held cameras, suggest that our method outperforms other state-of-the-art methods in accuracy.


Background modeling Object detection Non-static cameras Motion compensation Random algorithm Superpixel processing Gaussian mixture model 



Our thanks to the support from the National Nature Science Foundation of China (61502006, 61472002), the Natural Science Foundation of Anhui Province (1508085QF127) and the Natural Science Foundation of Anhui Higher Education Institutions of China (KJ2014A015).


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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Aihua Zheng
    • 1
  • Lei Zhang
    • 1
  • Wei Zhang
    • 1
  • Chenglong Li
    • 1
  • Jin Tang
    • 1
    • 2
  • Bin Luo
    • 1
    • 2
  1. 1.School of Computer Science and TechnologyAnHui UniversityHefeiChina
  2. 2.Key Laboratory of Industry Image Processing and Analysis in Anhui ProvinceHefeiChina

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