Full metadata record
DC pole | Hodnota | Jazyk |
---|---|---|
dc.contributor.author | Malawski, Filip | |
dc.contributor.author | Krupa, Marek | |
dc.contributor.editor | Skala, Václav | |
dc.date.accessioned | 2023-10-17T15:51:25Z | |
dc.date.available | 2023-10-17T15:51:25Z | |
dc.date.issued | 2023 | |
dc.identifier.citation | WSCG 2023: full papers proceedings: 1. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, p. 241-248. | en |
dc.identifier.isbn | 978-80-86943-32-9 | |
dc.identifier.issn | 2464–4617 (print) | |
dc.identifier.issn | 2464–4625 (CD/DVD) | |
dc.identifier.uri | http://hdl.handle.net/11025/54430 | |
dc.description.sponsorship | The 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.format | 8 s. | cs |
dc.format.mimetype | application/pdf | |
dc.language.iso | en | en |
dc.publisher | Václav Skala - UNION Agency | en |
dc.rights | © Václav Skala - UNION Agency | en |
dc.subject | časová segmentace | cs |
dc.subject | rozpoznávání akcí | cs |
dc.subject | sportovní analýza | cs |
dc.subject | oplocení | cs |
dc.subject | odhad pozice | cs |
dc.subject | pohybová analýza | cs |
dc.title | Temporal Segmentation of Actions in Fencing Footwork Training | en |
dc.type | konferenční příspěvek | cs |
dc.type | conferenceObject | en |
dc.rights.access | openAccess | en |
dc.type.version | publishedVersion | en |
dc.description.abstract-translated | Automatic 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.translated | temporal segmentation | en |
dc.subject.translated | action recognition | en |
dc.subject.translated | sports analysis | en |
dc.subject.translated | fencing | en |
dc.subject.translated | pose estimation | en |
dc.subject.translated | motion analysis | en |
dc.identifier.doi | https://www.doi.org/10.24132/CSRN.3301.28 | |
dc.type.status | Peer-reviewed | en |
Vyskytuje se v kolekcích: | WSCG 2023: Full Papers Proceedings |
Soubory připojené k záznamu:
Soubor | Popis | Velikost | Formát | |
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G02-full.pdf | Plný text | 1,68 MB | Adobe PDF | Zobrazit/otevřít |
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http://hdl.handle.net/11025/54430
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