Probabilistic Background Modelling for Sports Video Segmentation

Nikolas Ladas, Paris Kaimakis, Yiorgos Chrysanthou

Abstract

This paper introduces a segmentation algorithm based on the probabilistic modelling of the background color using a Lambertian formulation of the scene’s appearance. Central in our formulation is the computation of the degree of light visibility at the scene location depicted by each pixel. Because our approach specifically models the formation of shadows, segmentation results are of high accuracy. The quality of our results is further boosted by utilizing key observations about scene appearance. A qualitative and quantitative evaluation indicates that the proposed method performs better than commonly used segmentation algorithms, both for sports as well as for generic datasets.

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Paper Citation


in Harvard Style

Ladas N., Kaimakis P. and Chrysanthou Y. (2017). Probabilistic Background Modelling for Sports Video Segmentation . In Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2017) ISBN 978-989-758-225-7, pages 517-525. DOI: 10.5220/0006135505170525


in Bibtex Style

@conference{visapp17,
author={Nikolas Ladas and Paris Kaimakis and Yiorgos Chrysanthou},
title={Probabilistic Background Modelling for Sports Video Segmentation},
booktitle={Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2017)},
year={2017},
pages={517-525},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006135505170525},
isbn={978-989-758-225-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2017)
TI - Probabilistic Background Modelling for Sports Video Segmentation
SN - 978-989-758-225-7
AU - Ladas N.
AU - Kaimakis P.
AU - Chrysanthou Y.
PY - 2017
SP - 517
EP - 525
DO - 10.5220/0006135505170525