Searching for Variable-Speed Motions in Long Sequences of Motion Capture Data

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F19%3A00107162" target="_blank" >RIV/00216224:14330/19:00107162 - isvavai.cz</a>

Result on the web

<a href="http://dx.doi.org/10.1016/j.is.2018.04.002" target="_blank" >http://dx.doi.org/10.1016/j.is.2018.04.002</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.is.2018.04.002" target="_blank" >10.1016/j.is.2018.04.002</a>

Result language

angličtina

Original language name

Searching for Variable-Speed Motions in Long Sequences of Motion Capture Data

Original language description

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.

Czech name

—

Czech description

—

Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10200 - Computer and information sciences

Project

<a href="/en/project/GA16-18889S" target="_blank" >GA16-18889S: Big Data Analytics for Unstructured Data</a><br>

Continuities

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

Publication year

2019

Confidentiality

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

Name of the periodical

Information Systems

ISSN

0306-4379

e-ISSN

—

Volume of the periodical

80

Issue of the periodical within the volume

February

Country of publishing house

NL - THE KINGDOM OF THE NETHERLANDS

Number of pages

11

Pages from-to

148-158

UT code for WoS article

000454964800012

EID of the result in the Scopus database

2-s2.0-85045710372