BUDÍKOVÁ, Petra, Daniel KLEPÁČ, Dávid RUSNÁK and Milan SLOVÁK. Visual Exploration of Human Motion Data. Online. In Skopal, T., Falchi, F., Lokoč, J., Sapino, M.L., Bartolini, I., Patella, M. 15th International Conference on Similarity Search and Applications (SISAP 2022). Neuveden: Springer, Cham, 2022, p. 64-71. ISBN 978-3-031-17848-1. Available from: https://dx.doi.org/10.1007/978-3-031-17849-8_6.
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Basic information
Original name Visual Exploration of Human Motion Data
Authors BUDÍKOVÁ, Petra (203 Czech Republic, guarantor, belonging to the institution), Daniel KLEPÁČ (203 Czech Republic, belonging to the institution), Dávid RUSNÁK (703 Slovakia, belonging to the institution) and Milan SLOVÁK (203 Czech Republic, belonging to the institution).
Edition Neuveden, 15th International Conference on Similarity Search and Applications (SISAP 2022), p. 64-71, 8 pp. 2022.
Publisher Springer, Cham
Other information
Original language English
Type of outcome Proceedings paper
Field of Study 10200 1.2 Computer and information sciences
Confidentiality degree is not subject to a state or trade secret
Publication form electronic version available online
WWW URL
Impact factor Impact factor: 0.402 in 2005
RIV identification code RIV/00216224:14330/22:00128918
Organization unit Faculty of Informatics
ISBN 978-3-031-17848-1
ISSN 0302-9743
Doi http://dx.doi.org/10.1007/978-3-031-17849-8_6
UT WoS 000874756300006
Keywords in English Human motion data; Skeleton sequences; Visualization; Multimedia exploration; Explainability of similarity
Tags DISA, firank_B
Tags International impact, Reviewed
Changed by Changed by: RNDr. Pavel Šmerk, Ph.D., učo 3880. Changed: 28/3/2023 12:47.
Abstract
Human motion data are beginning to appear in many application domains, which brings a need to develop user-friendly motion processing applications. One of important open challenges is the presentation of high-dimensional spatio-temporal motion data to end users in a way that is easy to understand and allows fast browsing and exploration of the motion datasets. For many applications such as computer-assisted rehabilitation or motion learning, it is also very desirable to visualize the differences between two motion sequences. In this paper, we present a publicly available software tool that provides the visualization functionality for individual motion sequences, comparison of two motions, and exploration of large motion datasets.
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