Adaptive Motion Segmentation for Changing Background

Yepeng Guan

.

DOI: 10.4236/jsea.2009.22014                

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.

Keywords

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

Conflicts of Interest

The authors declare no conflicts of interest.

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