SEDMIDUBSKÝ, Jan and Pavel ZEZULA. Similarity Search in 3D Human Motion Data. Online. In International Conference on Multimedia Retrieval (ICMR). New York, NY, USA: ACM, 2019, p. 5-6. ISBN 978-1-4503-6765-3. Available from: https://dx.doi.org/10.1145/3323873.3326589.
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Basic information
Original name Similarity Search in 3D Human Motion Data
Authors SEDMIDUBSKÝ, Jan (203 Czech Republic, guarantor, belonging to the institution) and Pavel ZEZULA (203 Czech Republic, belonging to the institution).
Edition New York, NY, USA, International Conference on Multimedia Retrieval (ICMR), p. 5-6, 2 pp. 2019.
Publisher ACM
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
RIV identification code RIV/00216224:14330/19:00107369
Organization unit Faculty of Informatics
ISBN 978-1-4503-6765-3
Doi http://dx.doi.org/10.1145/3323873.3326589
UT WoS 000482188900003
Keywords in English motion capture data;3D skeleton sequence;similarity search;subsequence matching;annotation;action detection;stream processing
Tags DISA
Tags International impact, Reviewed
Changed by Changed by: doc. RNDr. Jan Sedmidubský, Ph.D., učo 60474. Changed: 15/4/2020 10:23.
Abstract
Motion capture technologies can digitize human movements into a discrete sequence of 3D skeletons. Such spatio-temporal data have a great application potential in many fields, ranging from computer animation, through security and sports to medicine, but their computerized processing is a difficult problem. The objective of this tutorial is to explain fundamental principles and technologies designed for searching, subsequence matching, classification and action detection in the 3D human motion data. These operations inherently require the concept of similarity to determine the degree of accordance between pairs of 3D skeleton sequences. Such similarity can be modeled using a generic approach of metric space by extracting effective deep features and comparing them by efficient distance functions. The metric-space approach also enables applying traditional index structures to efficiently access large datasets of skeleton sequences. We demonstrate the functionality of selected motion-processing operations by interactive web applications.
Links
GA19-02033S, research and development projectName: Vyhledávání, analytika a anotace datových toků lidských pohybů
Investor: Czech Science Foundation
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