Journal
IMAGE AND VISION COMPUTING
Volume 24, Issue 5, Pages 473-482Publisher
ELSEVIER
DOI: 10.1016/j.imavis.2006.01.018
Keywords
foreground segmentation; background subtraction; mixture models; EM algorithm; maximum a posteriori (MAP)
Ask authors/readers for more resources
Robust and real-time foreground segmentation is a crucial topic in many computer vision applications. Background subtraction is a typical approach to segment foreground by comparing each new frame with a learned model of the scene background in image sequences taken from a static camera. In this paper, we propose a flexible method to estimate the background model with the finite Gaussian mixture model. A stochastic approximation procedure is used to recursively estimate the parameters of the Gaussian mixture model, and to simultaneously obtain the asymptotically optimal number of the mixture components. Our method is highly memory and time efficient. Moreover, it can effectively deal with the many scenes, such as the indoor scene, the outdoor scene, and the clutter scene. The experimental results show our method is efficient and effective. (C) 2006 Elsevier B.V. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available