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DC poleHodnotaJazyk
dc.contributor.authorMalawski, Filip
dc.contributor.authorKrupa, Marek
dc.contributor.editorSkala, Václav
dc.date.accessioned2023-10-17T15:51:25Z
dc.date.available2023-10-17T15:51:25Z
dc.date.issued2023
dc.identifier.citationWSCG 2023: full papers proceedings: 1. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, p. 241-248.en
dc.identifier.isbn978-80-86943-32-9
dc.identifier.issn2464–4617 (print)
dc.identifier.issn2464–4625 (CD/DVD)
dc.identifier.urihttp://hdl.handle.net/11025/54430
dc.description.sponsorshipThe research presented in this paper was supported by the National Centre for Research and Development (NCBiR) under Grant No. LIDER/37/0198/L 12/20/NCBR/2021. We also thank Aramis Fencing School (aramis.pl) for providing experts’ consultations.en
dc.format8 s.cs
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherVáclav Skala - UNION Agencyen
dc.rights© Václav Skala - UNION Agencyen
dc.subjectčasová segmentacecs
dc.subjectrozpoznávání akcícs
dc.subjectsportovní analýzacs
dc.subjectoplocenícs
dc.subjectodhad pozicecs
dc.subjectpohybová analýzacs
dc.titleTemporal Segmentation of Actions in Fencing Footwork Trainingen
dc.typekonferenční příspěvekcs
dc.typeconferenceObjecten
dc.rights.accessopenAccessen
dc.type.versionpublishedVersionen
dc.description.abstract-translatedAutomatic analysis of actions in sports training can provide useful feedback for athletes. Fencing is one of the sports disciplines in which the correct technique for performing actions is very important. For any practical appli cation, temporal segmentation of movement in continuous training is crucial. In this work, we consider detecting and classifying actions in a sequence of fencing footwork exercises. We apply pose estimation to RGB videos and then we perform per-frame motion classification, using both classical machine learning and deep learning methods. Using sequences of frames with the same class we find data segments with specific actions. For evaluation, we provide extended manual labels for a fencing footwork dataset previously used in other works. Results indicate that the proposed methods are effective at detecting four footwork actions, obtaining 0.98 F1 score for recognition of action segments and 0.92 F1 score for per-frame classification. In the evaluation of our approach, we provide also a comparison with other data modalities, including depth-based pose estimation and inertial signals. Finally, we include an example of qualitative analysis of the performance of detected actions, to show how this approach can be used for training support.en
dc.subject.translatedtemporal segmentationen
dc.subject.translatedaction recognitionen
dc.subject.translatedsports analysisen
dc.subject.translatedfencingen
dc.subject.translatedpose estimationen
dc.subject.translatedmotion analysisen
dc.identifier.doihttps://www.doi.org/10.24132/CSRN.3301.28
dc.type.statusPeer-revieweden
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