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Multi-sensor background subtraction by fusing multiple region-based probabilistic classifiers
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DOI：10.1016/j.patrec.2013.09.022.png)
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Abstract
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In the recent years, the computer vision community has shown great interest on depth-based applications thanks to the performance and flexibility of the new generation of RGB-D imagery. In this paper, we present an efficient background subtraction algorithm based on the fusion of multiple region-based classifiers that processes depth and color data provided by RGB-D cameras. Foreground objects are detected by combining a region-based foreground prediction (based on depth data) with different background models (based on a Mixture of Gaussian algorithm) providing color and depth descriptions of the scene at pixel and region level. The information given by these modules is fused in a mixture of experts fashion to improve the foreground detection accuracy. The main contributions of the paper are the region-based models of both background and foreground, built from the depth and color data. The obtained results using different database sequences demonstrate that the proposed approach leads to a higher detection accuracy with respect to existing state-of-the-art techniques. (C) 2013 Elsevier B.V. All rights reserved.
Keywords:
Region-based background modeling
Foreground prediction
Mixture of Gaussians
Mixture of experts
Mean shift
RGB-D cameras
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