Fast Subsequence Matching in Motion Capture Data

D 2017

Fast Subsequence Matching in Motion Capture Data

SEDMIDUBSKÝ, Jan, Pavel ZEZULA a Jan ŠVEC

Základní údaje

Originální název

Fast Subsequence Matching in Motion Capture Data

Autoři

SEDMIDUBSKÝ, Jan (203 Česká republika, garant, domácí), Pavel ZEZULA (203 Česká republika, domácí) a Jan ŠVEC (203 Česká republika)

Vydání

Cham, 21st European Conference on Advances in Databases and Information Systems, od s. 59-72, 14 s. 2017

Nakladatel

Springer

Další údaje

Jazyk

angličtina

Typ výsledku

Stať ve sborníku

Obor

10201 Computer sciences, information science, bioinformatics

Utajení

není předmětem státního či obchodního tajemství

Forma vydání

tištěná verze "print"

Kód RIV

RIV/00216224:14330/17:00094760

Organizační jednotka

Fakulta informatiky

ISBN

978-3-319-66916-8

DOI

http://dx.doi.org/10.1007/978-3-319-66917-5_5

UT WoS

000463611400005

Klíčová slova anglicky

subsequence matching; motion capture data; content-based retrieval; similarity measure; segmentation; indexing

Štítky

core_B, DISA, firank_B

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 14. 5. 2020 15:14, RNDr. Pavel Šmerk, Ph.D.

Anotace

V originále

Motion capture data digitally represent human movements by sequences of body configurations in time. Subsequence matching in such spatio-temporal data is difficult as query-relevant motions can vary in lengths and occur arbitrarily in a very long motion. To deal with these problems, we propose a new subsequence matching approach which (1) partitions both short query and long data motion into fixed-size segments that overlap only partly, (2) uses an effective similarity measure to efficiently retrieve data segments that are the most similar to query segments, and (3) localizes the most query-relevant subsequences within extended and merged retrieved segments in a four-step postprocessing phase. The whole retrieval process is effective and fast in comparison with related work. A real-life 68-minute data motion can be searched in about 1s with the average precision of 87.98% for 5-NN queries.

Návaznosti

GBP103/12/G084, projekt VaV

Název: Centrum pro multi-modální interpretaci dat velkého rozsahu

Investor: Grantová agentura ČR, Centrum pro multi-modální interpretaci dat velkého rozsahu

Citovat

SEDMIDUBSKÝ, Jan, Pavel ZEZULA a Jan ŠVEC. Fast Subsequence Matching in Motion Capture Data. In 21st European Conference on Advances in Databases and Information Systems. Cham: Springer, 2017, s. 59-72. ISBN 978-3-319-66916-8. Dostupné z: https://dx.doi.org/10.1007/978-3-319-66917-5_5.

@inproceedings{1381785,
   author = {Sedmidubský, Jan and Zezula, Pavel and Švec, Jan},
   address = {Cham},
   booktitle = {21st European Conference on Advances in Databases and Information Systems},
   doi = {http://dx.doi.org/10.1007/978-3-319-66917-5_5},
   keywords = {subsequence matching; motion capture data; content-based retrieval; similarity measure; segmentation; indexing},
   howpublished = {tištěná verze "print"},
   language = {eng},
   location = {Cham},
   isbn = {978-3-319-66916-8},
   pages = {59-72},
   publisher = {Springer},
   title = {Fast Subsequence Matching in Motion Capture Data},
   year = {2017}
}

TY  - JOUR
ID  - 1381785
AU  - Sedmidubský, Jan - Zezula, Pavel - Švec, Jan
PY  - 2017
TI  - Fast Subsequence Matching in Motion Capture Data
PB  - Springer
CY  - Cham
SN  - 9783319669168
KW  - subsequence matching
KW  - motion capture data
KW  - content-based retrieval
KW  - similarity measure
KW  - segmentation
KW  - indexing
N2  - Motion capture data digitally represent human movements by sequences of body configurations in time. Subsequence matching in such spatio-temporal data is difficult as query-relevant motions can vary in lengths and occur arbitrarily in a very long motion. To deal with these problems, we propose a new subsequence matching approach which (1) partitions both short query and long data motion into fixed-size segments that overlap only partly, (2) uses an effective similarity measure to efficiently retrieve data segments that are the most similar to query segments, and (3) localizes the most query-relevant subsequences within extended and merged retrieved segments in a four-step postprocessing phase. The whole retrieval process is effective and fast in comparison with related work. A real-life 68-minute data motion can be searched in about 1s with the average precision of 87.98% for 5-NN queries.
ER  -

SEDMIDUBSKÝ, Jan, Pavel ZEZULA a Jan ŠVEC. Fast Subsequence Matching in Motion Capture Data. In \textit{21st European Conference on Advances in Databases and Information Systems}. Cham: Springer, 2017, s.~59-72. ISBN~978-3-319-66916-8. Dostupné z: https://dx.doi.org/10.1007/978-3-319-66917-5\_{}5.

Podrobný výpis o publikaci