GPU Implementation of Extended Gaussian Mixture Model for Background Subtraction
Department of Computer Science, University of Science, Ho Chi Minh City, Viet Nam
IEEE RIVF International Conference on Computing and Communication Technologies, Research, Innovation, and Vision for the Future (RIVF), 2010
@conference{pham2010gpu,
title={GPU implementation of Extended Gaussian mixture model for Background subtraction},
author={Pham, V. and Vo, P. and Hung, V.T.},
booktitle={Computing and Communication Technologies, Research, Innovation, and Vision for the Future (RIVF), 2010 IEEE RIVF International Conference on},
pages={1–4},
organization={IEEE}
}
Although trivial background subtraction (BGS) algorithms (e.g. frame differencing, running average…) can perform quite fast, they are not robust enough to be used in various computer vision problems. Some complex algorithms usually give better results, but are too slow to be applied to real-time systems. We propose an improved version of the Extended Gaussian mixture model that utilizes the computational power of Graphics Processing Units (GPUs) to achieve real-time performance. Experiments show that our implementation running on a low-end GeForce 9600GT GPU provides at least 10x speedup. The frame rate is greater than 50 frames per second (fps) for most of the tests, even on HD video formats.
May 3, 2011 by hgpu
