Speeded up Gaussian mixture model algorithm for background subtraction

Gorur, P and Amrutur, B (2011) Speeded up Gaussian mixture model algorithm for background subtraction. In: 2011 8th IEEE International Conference on Advanced Video and Signal Based Surveillance, August 30-September 02, Klagenfurt.

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Abstract

Adaptive Gaussian Mixture Models (GMM) have been one of the most popular and successful approaches to perform foreground segmentation on multimodal background scenes. However, the good accuracy of the GMM algorithm comes at a high computational cost. An improved GMM technique was proposed by Zivkovic to reduce computational cost by minimizing the number of modes adaptively. In this paper, we propose a modification to his adaptive GMM algorithm that further reduces execution time by replacing expensive floating point computations with low cost integer operations. To maintain accuracy, we derive a heuristic that computes periodic floating point updates for the GMM weight parameter using the value of an integer counter. Experiments show speedups in the range of 1.33 - 1.44 on standard video datasets where a large fraction of pixels are multimodal.

Item Type:	Conference Paper
Publisher:	IEEE
Additional Information:	Copyright of this article belongs to IEEE.
Department/Centre:	Division of Electrical Sciences > Electrical Communication Engineering
Date Deposited:	01 Apr 2013 07:01
Last Modified:	01 Apr 2013 07:01
URI:	http://eprints.iisc.ac.in/id/eprint/46131

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