Dynamic video segmentation via a novel recursive Bayesian learning method

Segmentation of an interesting target from a dynamic video has been an important research topic in computer vision. In this work, we present a novel recursive Bayesian learning method for dynamic video segmentation. In the algorithm, each frame pixel is represented as layered normal distributions and the recursive Bayesian estimation is used to update the background parameters so as to obtain a robust background model. In the segmentation, foreground is separated by simple background subtraction method firstly. And then, a local texture correlation operator is proposed to remove vacancies in the separated foreground to refine the segmentation result. Experiments with two typical video clips are used to demonstrate that the proposed method can outperform traditional methods in both segmentation result and converging speed.