SEDMIDUBSKÝ, Jan, Petr ELIÁŠ and Pavel ZEZULA. Searching for Variable-Speed Motions in Long Sequences of Motion Capture Data. Online. Information Systems. 2019, vol. 80, February, p. 148-158. ISSN 0306-4379. Available from: https://dx.doi.org/10.1016/j.is.2018.04.002. [citováno 2024-04-24]
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Basic information
Original name Searching for Variable-Speed Motions in Long Sequences of Motion Capture Data
Authors SEDMIDUBSKÝ, Jan (203 Czech Republic, guarantor, belonging to the institution), Petr ELIÁŠ (203 Czech Republic, belonging to the institution) and Pavel ZEZULA (203 Czech Republic, belonging to the institution)
Edition Information Systems, 2019, 0306-4379.
Other information
Original language English
Type of outcome Article in a journal
Field of Study 10200 1.2 Computer and information sciences
Country of publisher Netherlands
Confidentiality degree is not subject to a state or trade secret
WWW URL
Impact factor Impact factor: 2.466
RIV identification code RIV/00216224:14330/19:00107162
Organization unit Faculty of Informatics
Doi http://dx.doi.org/10.1016/j.is.2018.04.002
UT WoS 000454964800012
Keywords in English Content-based retrieval;Motion capture data;Subsequence matching;Speed-invariant retrieval;Similarity measure;Hierarchical segmentation;Indexing
Tags DISA
Tags International impact, Reviewed
Changed by Changed by: doc. RNDr. Jan Sedmidubský, Ph.D., učo 60474. Changed: 16/4/2019 07:35.
Abstract
Motion capture data digitally represent human movements by sequences of body configurations in time. Subsequence searching in long sequences of such spatio-temporal data is difficult as query-relevant motions can vary in execution speeds and styles and can occur anywhere in a very long data sequence. To deal with these problems, we employ a fast and effective similarity measure that is elastic. The property of elasticity enables matching of two overlapping but slightly misaligned subsequences with a high confidence. Based on the elasticity, the long data sequence is partitioned into overlapping segments that are organized in multiple levels. The number of levels and sizes of overlaps are optimized to generate a modest number of segments while being able to trace an arbitrary query. In a retrieval phase, a query is always represented as a single segment and fast matched against segments within a relevant level without any costly post-processing. Moreover, visiting adjacent levels makes possible subsequence searching of time-warped (i.e., faster or slower executed) queries. To efficiently search on a large scale, segment features can be binarized and segmentation levels independently indexed. We experimentally demonstrate effectiveness and efficiency of the proposed approach for subsequence searching on a real-life dataset.
Links
GA16-18889S, research and development projectName: Analytika pro velká nestrukturovaná data (Acronym: Big Data Analytics for Unstructured Data)
Investor: Czech Science Foundation
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