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Authors: Brennan Gebotys ; Alexander Wong and David Clausi

Affiliation: Systems Design Engineering, University of Waterloo, Waterloo, Canada

Keyword(s): Computer Vision, Neural Networks, Pose Estimation, Optical Flow, Annotation.

Abstract: Due to the wide range of applications for human pose estimation including sports analytics and more, research has optimized pose estimation models to achieve high accuracies when trained on large human pose datasets. However, applying these learned models to datasets that are from a different domain (which is usually the goal for many real-world applications) usually leads to a large decrease in accuracy which is not acceptable. To achieve acceptable results, a large number of annotations is still required which can be very expensive. In this research, we leverage the fact that many pose estimation datasets are derived from individual frames of a video and use this information to develop and implement an efficient pose annotation method. Our method uses the temporal motion between frames of a video to propagate ground truth keypoints across neighbouring frames to generate more annotations to provide efficient POse annotation using Optical Flow (POOF). We find POOF achieves the best p erformance when used in different domains than the pretrained domain. We show that in the case of a real-world hockey dataset, using POOF can achieve 75% accuracy (a +15% improvement, compared to using COCO-pretrained weights) with a very small number of ground truth annotations. (More)

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Paper citation in several formats:
Gebotys, B.; Wong, A. and Clausi, D. (2021). POOF: Efficient Goalie Pose Annotation using Optical Flow. In Proceedings of the 9th International Conference on Sport Sciences Research and Technology Support - icSPORTS; ISBN 978-989-758-539-5; ISSN 2184-3201, SciTePress, pages 116-122. DOI: 10.5220/0010657000003059

@conference{icsports21,
author={Brennan Gebotys. and Alexander Wong. and David Clausi.},
title={POOF: Efficient Goalie Pose Annotation using Optical Flow},
booktitle={Proceedings of the 9th International Conference on Sport Sciences Research and Technology Support - icSPORTS},
year={2021},
pages={116-122},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010657000003059},
isbn={978-989-758-539-5},
issn={2184-3201},
}

TY - CONF

JO - Proceedings of the 9th International Conference on Sport Sciences Research and Technology Support - icSPORTS
TI - POOF: Efficient Goalie Pose Annotation using Optical Flow
SN - 978-989-758-539-5
IS - 2184-3201
AU - Gebotys, B.
AU - Wong, A.
AU - Clausi, D.
PY - 2021
SP - 116
EP - 122
DO - 10.5220/0010657000003059
PB - SciTePress

- Science and Technology Publications, Lda.
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