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[3] T. Horprasert, D. Harwood, and L. S. Davis, “A statistical approach for real-time robust background subtraction and shadow detection,” Proceedings of IEEE International Conference on Computer Vision Frame-Rate Workshop, pp. 1-19, September 1999.

has been cited by the following article:

• TITLE: Adaptive Motion Segmentation for Changing Background

  AUTHORS: Yepeng Guan

  KEYWORDS: Motion Segmentation, Background Update, Background Subtraction, Motion Variation, Shadow Suppression

  JOURNAL NAME: Journal of Software Engineering and Applications, Vol.2 No.2, July 15, 2009

  ABSTRACT: Segmentation of moving objects efficiently from video sequence is very important for many applications. Background subtraction is a common method typically used to segment moving objects in image sequences taken from a statistic camera. Some existing algorithms cannot adapt to changing circumstances and require manual calibration in terms of specification of parameters or some hypotheses for changing background. An adaptive motion segmentation method is developed according to motion variation and chromatic characteristics, which prevents undesired corruption of the background model and does not consider the adaptation coefficient. RGB color space is selected instead of introducing complex color models to segment moving objects and suppress shadows. A color ratio for 4-connected neighbors of a pixel and multi-scale wavelet transformation are combined to suppress shadows. The mentioned approach is scene-independent and high correct segmentation. It has been shown that the approach is robust and efficient to detect moving objects by experiments.