Adaptive background model for non-static background subtraction by estimation of the color change ratio
Background modeling, a preliminary processing step for foreground detection, is a challenging task because of the complexity and variety of background regions and unexpected scenarios such as sudden illumination changes, waving trees, rippling water, etc. In this work, we develop a pixel-based background modeling method that uses a probabilistic approach by means of changing color sequences. This method uses two background models in tandem. The first model uses a static background, which is obtained via a probabilistic approach and is a standard from which the foreground is extracted. The second method uses an adaptive background, which is modeled by the degree of color change. This background functions as an additional standard from which the foreground is extracted and is appropriate for eliminating non-static background elements. These models enable the developed method to automatically adapt to various environments. The algorithm was tested on various video sequences and its performance was evaluated by comparison with other state-of-the-art background subtraction methods.