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Authors: Nikolas Ladas 1 ; Paris Kaimakis 2 and Yiorgos Chrysanthou 1

Affiliations: 1 University of Cyprus, Cyprus ; 2 University of Central Lancashire Cyprus, Cyprus

Keyword(s): Segmentation, Background Modelling, Shadow Detection, Visibility Decomposition.

Related Ontology Subjects/Areas/Topics: Applications and Services ; Color and Texture Analyses ; Computer Vision, Visualization and Computer Graphics ; Entertainment Imaging Applications ; Image and Video Analysis ; Image Formation and Preprocessing ; Image Formation, Acquisition Devices and Sensors ; Segmentation and Grouping

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.

CC BY-NC-ND 4.0

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Paper citation in several formats:
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; ISSN 2184-4321, pages 517-525. DOI: 10.5220/0006135505170525

@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},
issn={2184-4321},
}

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
IS - 2184-4321
AU - Ladas, N.
AU - Kaimakis, P.
AU - Chrysanthou, Y.
PY - 2017
SP - 517
EP - 525
DO - 10.5220/0006135505170525

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