Fast Subsequence Matching in Motion Capture Data

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F17%3A00094760" target="_blank" >RIV/00216224:14330/17:00094760 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/978-3-319-66917-5_5" target="_blank" >http://dx.doi.org/10.1007/978-3-319-66917-5_5</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-66917-5_5" target="_blank" >10.1007/978-3-319-66917-5_5</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Fast Subsequence Matching in Motion Capture Data

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Fast Subsequence Matching in Motion Capture Data

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/GBP103%2F12%2FG084" target="_blank" >GBP103/12/G084: Centrum pro multi-modální interpretaci dat velkého rozsahu</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2017

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

21st European Conference on Advances in Databases and Information Systems

ISBN

9783319669168

ISSN

—

e-ISSN

—

Počet stran výsledku

14

Strana od-do

59-72

Název nakladatele

Springer

Místo vydání

Cham

Místo konání akce

Nicosia, Cyprus

Datum konání akce

24. 9. 2017

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000463611400005