Work

Separating background and foregroundin video based on a nonparametric Bayesian model

Year: 2011

Type: conference-paper

Abstract: 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.

Source: IEEE Statistical Signal Processing Workshop (SSP)

DOI: 10.1109/ssp.2011.5967692

Authors: Xinghao Ding, Lawrence Carin

Institution: Duke University

Language: English

Topic: Video Surveillance and Tracking Methods

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W2109685060

FWCI: 0.477

Cites: 15

Cited by: 2

Related to: 10