SEDMIDUBSKÝ, Jan, Petr ELIÁŠ a Pavel ZEZULA. Searching for Variable-Speed Motions in Long Sequences of Motion Capture Data. Information Systems, 2019, roč. 80, February, s. 148-158. ISSN 0306-4379. doi:10.1016/
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Základní údaje
Originální název Searching for Variable-Speed Motions in Long Sequences of Motion Capture Data
Autoři SEDMIDUBSKÝ, Jan (203 Česká republika, garant, domácí), Petr ELIÁŠ (203 Česká republika, domácí) a Pavel ZEZULA (203 Česká republika, domácí).
Vydání Information Systems, 2019, 0306-4379.
Další údaje
Originální jazyk angličtina
Typ výsledku Článek v odborném periodiku
Obor 10200 1.2 Computer and information sciences
Stát vydavatele Nizozemsko
Utajení není předmětem státního či obchodního tajemství
Impakt faktor Impact factor: 2.066 v roce 2018
Organizační jednotka Fakulta informatiky
UT WoS 000454964800012
Klíčová slova anglicky Content-based retrieval;Motion capture data;Subsequence matching;Speed-invariant retrieval;Similarity measure;Hierarchical segmentation;Indexing
Štítky DISA
Příznaky Mezinárodní význam, Recenzováno
Změnil Změnil: RNDr. Jan Sedmidubský, Ph.D., učo 60474. Změněno: 16. 4. 2019 07:35.
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.
GA16-18889S, projekt VaVNázev: Analytika pro velká nestrukturovaná data (Akronym: Big Data Analytics for Unstructured Data)
Investor: Grantová agentura ČR, Standardní projekty
VytisknoutZobrazeno: 22. 1. 2020 22:22