A novel recursive Bayesian learning method for video segmentation
Qingsong Zhu, Zhan Ping Song · 2010
This work presents a novel 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 to obtain a robust background model. In the segmentation, foreground is separated by simple background subtraction method firstly. And then, a local texture correlation method is introduced to remove vacancies in the separated foreground to achieve better segmentation result. Experimental results on two typical video clips are used to show the proposed method can outperform traditional methods in both segmentation result and converging speed.