Illumination invariant background model using mixture of gaussians and SURF features

Authors:
Munir Shah;Jeremiah Deng;Brendon Woodford
Affiliations:
Department of Information Science, University of Otago, Dunedin, New Zealand;Department of Information Science, University of Otago, Dunedin, New Zealand;Department of Information Science, University of Otago, Dunedin, New Zealand
Venue:
ACCV'12 Proceedings of the 11th international conference on Computer Vision - Volume Part I
Year:
2012

Citing 4
Cited 0

Improved Adaptive Gaussian Mixture Model for Background Subtraction

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Effective Gaussian Mixture Learning for Video Background Subtraction

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Speeded-Up Robust Features (SURF)

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Flexible background mixture models for foreground segmentation

Image and Vision Computing

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Abstract

The Mixture of Gaussians (MoG) is a frequently used method for foreground-background separation. In this paper, we propose an on-line learning framework that allows the MoG algorithm to quickly adapt its localized parameters. Our main contributions are: local parameter adaptations, a feedback based updating method for stopped objects, and hierarchical SURF features matching based ghosts and local illumination suppression method. The proposed model is rigorously tested and compared with several previous models on BMC data set and has shown significant performance improvements.