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  Two stream network for stroke detection in table tennis

Zahra, A., & Martin, P.-E. (2021). Two stream network for stroke detection in table tennis. In Proceedings of MediaEval 2021: Multimedia Benchmark Workshop. doi:10.48550/arXiv.2112.12073.

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Item Permalink: https://hdl.handle.net/21.11116/0000-000A-4B11-F Version Permalink: https://hdl.handle.net/21.11116/0000-000B-FE95-F
Genre: Conference Paper

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Zahra_Two_MediaEval_2021.pdf (Any fulltext), 666KB
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Zahra_Two_MediaEval_2021.pdf
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2021
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Copyright 2021 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
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https://creativecommons.org/licenses/by/4.0/

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 Creators:
Zahra, Anam1, Author                 
Martin, Pierre-Etienne1, Author                 
Affiliations:
1Department of Comparative Cultural Psychology, Max Planck Institute for Evolutionary Anthropology, Max Planck Society, ou_3040267              

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Free keywords: Computer Science, Computer Vision and Pattern Recognition, cs.CV,Computer Science, Learning, cs.LG,Computer Science, Multimedia, cs.MM
 Abstract: This paper presents a table tennis stroke detection method from videos. The
method relies on a two-stream Convolutional Neural Network processing in
parallel the RGB Stream and its computed optical flow. The method has been
developed as part of the MediaEval 2021 benchmark for the Sport task. Our
contribution did not outperform the provided baseline on the test set but has
performed the best among the other participants with regard to the mAP metric.

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Language(s): eng - English
 Dates: 2021-12-16
 Publication Status: Published online
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.48550/arXiv.2112.12073
Other: hal-03482217
 Degree: -

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Title: MediaEval 2021 Workshop
Place of Event: Online
Start-/End Date: 2021-12-13 - 2021-12-15

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Title: Proceedings of MediaEval 2021 : Multimedia Benchmark Workshop
Source Genre: Proceedings
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Publ. Info: -
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: - Identifier: URN: https://multimediaeval.github.io/editions/2021/