A Key-Pose Similarity Algorithm for Motion Data Retrieval

D 2013

A Key-Pose Similarity Algorithm for Motion Data Retrieval

SEDMIDUBSKÝ, Jan, Jakub VALČÍK and Pavel ZEZULA

Basic information

Original name

A Key-Pose Similarity Algorithm for Motion Data Retrieval

Authors

SEDMIDUBSKÝ, Jan (203 Czech Republic, guarantor, belonging to the institution), Jakub VALČÍK (203 Czech Republic, belonging to the institution) and Pavel ZEZULA (203 Czech Republic, belonging to the institution)

Edition

Switzerland, Proceedings of 12th International Conference on Advanced Concepts for Intelligent Vision Systems (ACIVS 2013), LNCS 8192, p. 669-681, 13 pp. 2013

Publisher

Springer International Publishing

Other information

Language

English

Type of outcome

Stať ve sborníku

Field of Study

10201 Computer sciences, information science, bioinformatics

Country of publisher

Poland

Confidentiality degree

není předmětem státního či obchodního tajemství

Publication form

printed version "print"

Impact factor

Impact factor: 0.402 in 2005

RIV identification code

RIV/00216224:14330/13:00065724

Organization unit

Faculty of Informatics

ISBN

978-3-319-02894-1

ISSN

DOI

http://dx.doi.org/10.1007/978-3-319-02895-8_60

UT WoS

000332973500060

Keywords in English

motion capture data; motion retrieval; subsequence retrieval; similar sub-motions

Abstract

V originále

Analysis of human motion data is an important task in many research fields such as sports, medicine, security, and computer animation. In order to fully exploit motion databases for further processing, effective and efficient retrieval methods are needed. However, such task is difficult primarily due to complex spatio-temporal variances of individual human motions and the rapidly increasing volume of motion data. In this paper, we propose a universal content-based subsequence retrieval algorithm for indexing and searching motion data. The algorithm is able to examine database motions and locate all their sub-motions that are similar to a query motion example. We illustrate the algorithm usability by indexing motion features in form of joint-angle rotations extracted from a real-life 68-minute human motion database. We analyse the algorithm time complexity and evaluate retrieval effectiveness by comparing the search results against user-defined ground truth. The algorithm is also incorporated in an online web application facilitating query definition and visualization of search results.

Links

GBP103/12/G084, research and development project

Name: Centrum pro multi-modální interpretaci dat velkého rozsahu

Investor: Czech Science Foundation

VG20122015073, research and development project

Name: Efektivní vyhledávání v rozsáhlých biometrických datech (Acronym: EFBIO)

Investor: Ministry of the Interior of the CR

Citovat

SEDMIDUBSKÝ, Jan, Jakub VALČÍK and Pavel ZEZULA. A Key-Pose Similarity Algorithm for Motion Data Retrieval. In J. Blanc-Talon et al. (Eds.). Proceedings of 12th International Conference on Advanced Concepts for Intelligent Vision Systems (ACIVS 2013), LNCS 8192. Switzerland: Springer International Publishing, 2013, p. 669-681. ISBN 978-3-319-02894-1. Available from: https://dx.doi.org/10.1007/978-3-319-02895-8_60.

@inproceedings{1117913,
   author = {Sedmidubský, Jan and Valčík, Jakub and Zezula, Pavel},
   address = {Switzerland},
   booktitle = {Proceedings of 12th International Conference on Advanced Concepts for Intelligent Vision Systems (ACIVS 2013), LNCS 8192},
   doi = {http://dx.doi.org/10.1007/978-3-319-02895-8_60},
   editor = {J. Blanc-Talon et al. (Eds.)},
   keywords = {motion capture data; motion retrieval; subsequence retrieval; similar sub-motions},
   howpublished = {tištěná verze "print"},
   language = {eng},
   location = {Switzerland},
   isbn = {978-3-319-02894-1},
   pages = {669-681},
   publisher = {Springer International Publishing},
   title = {A Key-Pose Similarity Algorithm for Motion Data Retrieval},
   year = {2013}
}

TY  - JOUR
ID  - 1117913
AU  - Sedmidubský, Jan - Valčík, Jakub - Zezula, Pavel
PY  - 2013
TI  - A Key-Pose Similarity Algorithm for Motion Data Retrieval
PB  - Springer International Publishing
CY  - Switzerland
SN  - 9783319028941
KW  - motion capture data
KW  - motion retrieval
KW  - subsequence retrieval
KW  - similar sub-motions
N2  - Analysis of human motion data is an important task in many research fields such as sports, medicine, security, and computer animation. In order to fully exploit motion databases for further processing, effective and efficient retrieval methods are needed. However, such task is difficult primarily due to complex spatio-temporal variances of individual human motions and the rapidly increasing volume of motion data. In this paper, we propose a universal content-based subsequence retrieval algorithm for indexing and searching motion data. The algorithm is able to examine database motions and locate all their sub-motions that are similar to a query motion example. We illustrate the algorithm usability by indexing motion features in form of joint-angle rotations extracted from a real-life 68-minute human motion database. We analyse the algorithm time complexity and evaluate retrieval effectiveness by comparing the search results against user-defined ground truth. The algorithm is also incorporated in an online web application facilitating query definition and visualization of search results.
ER  -

SEDMIDUBSKÝ, Jan, Jakub VALČÍK and Pavel ZEZULA. A Key-Pose Similarity Algorithm for Motion Data Retrieval. In J. Blanc-Talon et al. (Eds.). \textit{Proceedings of 12th International Conference on Advanced Concepts for Intelligent Vision Systems (ACIVS 2013), LNCS 8192}. Switzerland: Springer International Publishing, 2013, p.~669-681. ISBN~978-3-319-02894-1. Available from: https://dx.doi.org/10.1007/978-3-319-02895-8\_{}60.

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