SPEED21: Speed Climbing Motion Dataset

D 2021

SPEED21: Speed Climbing Motion Dataset

ELIÁŠ, Petr, Veronika ŠKVARLOVÁ a Pavel ZEZULA

Základní údaje

Originální název

SPEED21: Speed Climbing Motion Dataset

Autoři

ELIÁŠ, Petr (203 Česká republika, domácí), Veronika ŠKVARLOVÁ (703 Slovensko, domácí) a Pavel ZEZULA (203 Česká republika, garant, domácí)

Vydání

New York, NY, United States, MMSports'21: Proceedings of the 4th International Workshop on Multimedia Content Analysis in Sports, od s. 43-50, 8 s. 2021

Nakladatel

Association for Computing Machinery

Další údaje

Jazyk

angličtina

Typ výsledku

Stať ve sborníku

Obor

10201 Computer sciences, information science, bioinformatics

Stát vydavatele

Spojené státy

Utajení

není předmětem státního či obchodního tajemství

Forma vydání

elektronická verze "online"

Odkazy

URL

Kód RIV

RIV/00216224:14330/21:00129011

Organizační jednotka

Fakulta informatiky

ISBN

978-1-4503-8670-8

DOI

http://dx.doi.org/10.1145/3475722.3482795

Klíčová slova anglicky

speed climbing; sports dataset; 2d skeleton series; k-nn search; similarity

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 6. 4. 2023 10:11, RNDr. Pavel Šmerk, Ph.D.

Anotace

V originále

With the recent advances in computer vision and deep learning, the research interest in video-based and skeleton-based sports analysis is growing. Also, speed climbing as a sport is on the rise, being included as an Olympic sport in Tokyo 2020. This work aims to connect both of these worlds. First, a dataset of 362 speed climbing performances is provided for the community of domain experts and practitioners in human motion understanding and sports analysis. The dataset annotates pre-segmented performances of 55 world elite athletes in the form of 2D skeleton sequences extracted from world competition events videos. Secondly, a high descriptiveness and usability of 2D skeleton data is demonstrated in the search scenario that matches climbers by the similarities in their climbing style with high accuracy. The high k-NN search precision above 90 % is achieved by a synergic combination of suitable representation with a semi-dependent variant of Dynamic Time Warping (DTW). The proposed DTW variant computes distances separately across individual semantic body parts (e.g., hands and feet) whose atoms (joints or angles) are wired together for the temporal alignment.

Návaznosti

GA19-02033S, projekt VaV

Název: Vyhledávání, analytika a anotace datových toků lidských pohybů

Investor: Grantová agentura ČR, Searching, Mining, and Annotating Human Motion Streams

Citovat

ELIÁŠ, Petr, Veronika ŠKVARLOVÁ a Pavel ZEZULA. SPEED21: Speed Climbing Motion Dataset. Online. In Rainer Lienhart,Thomas B. Moeslund, Hideo Saito. MMSports'21: Proceedings of the 4th International Workshop on Multimedia Content Analysis in Sports. New York, NY, United States: Association for Computing Machinery, 2021, s. 43-50. ISBN 978-1-4503-8670-8. Dostupné z: https://dx.doi.org/10.1145/3475722.3482795.

@inproceedings{1808457,
   author = {Eliáš, Petr and Škvarlová, Veronika and Zezula, Pavel},
   address = {New York, NY, United States},
   booktitle = {MMSports'21: Proceedings of the 4th International Workshop on Multimedia Content Analysis in Sports},
   doi = {http://dx.doi.org/10.1145/3475722.3482795},
   editor = {Rainer Lienhart,Thomas B. Moeslund, Hideo Saito},
   keywords = {speed climbing; sports dataset; 2d skeleton series; k-nn search; similarity},
   howpublished = {elektronická verze "online"},
   language = {eng},
   location = {New York, NY, United States},
   isbn = {978-1-4503-8670-8},
   pages = {43-50},
   publisher = {Association for Computing Machinery},
   title = {SPEED21: Speed Climbing Motion Dataset},
   url = {https://dl.acm.org/doi/10.1145/3475722.3482795},
   year = {2021}
}

TY  - JOUR
ID  - 1808457
AU  - Eliáš, Petr - Škvarlová, Veronika - Zezula, Pavel
PY  - 2021
TI  - SPEED21: Speed Climbing Motion Dataset
PB  - Association for Computing Machinery
CY  - New York, NY, United States
SN  - 9781450386708
KW  - speed climbing
KW  - sports dataset
KW  - 2d skeleton series
KW  - k-nn search
KW  - similarity
UR  - https://dl.acm.org/doi/10.1145/3475722.3482795
N2  - With the recent advances in computer vision and deep learning, the research interest in video-based and skeleton-based sports analysis is growing. Also, speed climbing as a sport is on the rise, being included as an Olympic sport in Tokyo 2020. This work aims to connect both of these worlds. First, a dataset of 362 speed climbing performances is provided for the community of domain experts and practitioners in human motion understanding and sports analysis. The dataset annotates pre-segmented performances of 55 world elite athletes in the form of 2D skeleton sequences extracted from world competition events videos. Secondly, a high descriptiveness and usability of 2D skeleton data is demonstrated in the search scenario that matches climbers by the similarities in their climbing style with high accuracy. The high k-NN search precision above 90 % is achieved by a synergic combination of suitable representation with a semi-dependent variant of Dynamic Time Warping (DTW). The proposed DTW variant computes distances separately across individual semantic body parts (e.g., hands and feet) whose atoms (joints or angles) are wired together for the temporal alignment.
ER  -

ELIÁŠ, Petr, Veronika ŠKVARLOVÁ a Pavel ZEZULA. SPEED21: Speed Climbing Motion Dataset. Online. In Rainer Lienhart,Thomas B. Moeslund, Hideo Saito. \textit{MMSports'21: Proceedings of the 4th International Workshop on Multimedia Content Analysis in Sports}. New York, NY, United States: Association for Computing Machinery, 2021, s.~43-50. ISBN~978-1-4503-8670-8. Dostupné z: https://dx.doi.org/10.1145/3475722.3482795.

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