Separating background and foreground in video is a fundamental problem in computer vision. We present a Bayesian hierarchical model to address this challenge, and apply it to video with dynamic scenes. The model uses a nonparametric prior, a beta-bernoulli process, for both the background and foreground representation. Additionally, the model uses neighborhood information of each pixel to encourage group clustering of the foreground. A collapsed Gibbs sampler is used for efficient posterior inference. Experimental results show competitive performance of the proposed model. © 2011 IEEE.
|Original language||English (US)|
|Title of host publication||IEEE Workshop on Statistical Signal Processing Proceedings|
|Number of pages||4|
|State||Published - Sep 5 2011|