Video Segmentation via a Gaussian Switch Background Model and Higher Order Markov Random Fields

Martin Radolko, Enrico Gutzeit


Foreground-background segmentation in videos is an important low-level task needed for many different applications in computer vision. Therefore, a great variety of different algorithms have been proposed to deal with this problem, however none can deliver satisfactory results in all circumstances. Our approach combines an efficent novel Background Substraction algorithm with a higher order Markov Random Field (MRF) which can model the spatial relations between the pixels of an image far better than a simple pairwise MRF used in most of the state of the art methods. Afterwards, a runtime optimized Belief Propagation algorithm is used to compute an enhanced segmentation based on this model. Lastly, a local between Class Variance method is combined with this to enrich the data from the Background Substraction. To evaluate the results the difficult Wallflower data set is used.


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Paper Citation

in Harvard Style

Radolko M. and Gutzeit E. (2015). Video Segmentation via a Gaussian Switch Background Model and Higher Order Markov Random Fields . In Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2015) ISBN 978-989-758-089-5, pages 537-544. DOI: 10.5220/0005308505370544

in Bibtex Style

author={Martin Radolko and Enrico Gutzeit},
title={Video Segmentation via a Gaussian Switch Background Model and Higher Order Markov Random Fields},
booktitle={Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2015)},

in EndNote Style

JO - Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2015)
TI - Video Segmentation via a Gaussian Switch Background Model and Higher Order Markov Random Fields
SN - 978-989-758-089-5
AU - Radolko M.
AU - Gutzeit E.
PY - 2015
SP - 537
EP - 544
DO - 10.5220/0005308505370544