FootAndBall: Integrated Player and Ball Detector

Jacek Komorowski, Jacek Komorowski, Grzegorz Kurzejamski, Grzegorz Kurzejamski, Grzegorz Sarwas, Grzegorz Sarwas

2020

Abstract

The paper describes a deep neural network-based detector dedicated for ball and players detection in high resolution, long shot, video recordings of soccer matches. The detector, dubbed FootAndBall, has an efficient fully convolutional architecture and can operate on input video stream with an arbitrary resolution. It produces ball confidence map encoding the position of the detected ball, player confidence map and player bounding boxes tensor encoding players’ positions and bounding boxes. The network uses Feature Pyramid Network desing pattern, where lower level features with higher spatial resolution are combined with higher level features with bigger receptive field. This improves discriminability of small objects (the ball) as larger visual context around the object of interest is taken into account for the classification. Due to its specialized design, the network has two orders of magnitude less parameters than a generic deep neural network-based object detector, such as SSD or YOLO. This allows real-time processing of high resolution input video stream.

Paper Citation

in Harvard Style

Komorowski J., Kurzejamski G. and Sarwas G. (2020). FootAndBall: Integrated Player and Ball Detector. In Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 5: VISAPP; ISBN 978-989-758-402-2, SciTePress, pages 47-56. DOI: 10.5220/0008916000470056

in Bibtex Style

@conference{visapp20,
author={Jacek Komorowski and Grzegorz Kurzejamski and Grzegorz Sarwas},
title={FootAndBall: Integrated Player and Ball Detector},
booktitle={Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 5: VISAPP},
year={2020},
pages={47-56},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008916000470056},
isbn={978-989-758-402-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 5: VISAPP
TI - FootAndBall: Integrated Player and Ball Detector
SN - 978-989-758-402-2
AU - Komorowski J.
AU - Kurzejamski G.
AU - Sarwas G.
PY - 2020
SP - 47
EP - 56
DO - 10.5220/0008916000470056
PB - SciTePress