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Background segmentation with feedback: The Pixel-Based Adaptive Segmenter

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3 Author(s)
Hofmann, M. ; Inst. for Human-Machine Commun., Tech. Univ. Munchen, Munich, Germany ; Tiefenbacher, P. ; Rigoll, G.

In this paper we present a novel method for foreground segmentation. Our proposed approach follows a non-parametric background modeling paradigm, thus the background is modeled by a history of recently observed pixel values. The foreground decision depends on a decision threshold. The background update is based on a learning parameter. We extend both of these parameters to dynamic per-pixel state variables and introduce dynamic controllers for each of them. Furthermore, both controllers are steered by an estimate of the background dynamics. In our experiments, the proposed Pixel-Based Adaptive Segmenter (PBAS) outperforms most state-of-the-art methods.

Published in:

Computer Vision and Pattern Recognition Workshops (CVPRW), 2012 IEEE Computer Society Conference on

Date of Conference:

16-21 June 2012