Motion Images: An Effective Representation of Motion Capture
Data for Similarity Search

D 2015

Motion Images: An Effective Representation of Motion Capture Data for Similarity Search

ELIÁŠ, Petr, Jan SEDMIDUBSKÝ and Pavel ZEZULA

Basic information

Original name

Motion Images: An Effective Representation of Motion Capture Data for Similarity Search

Authors

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

Edition

Switzerland, Proceedings of 8th International Conference on Similarity Search and Applications (SISAP 2015), LNCS 9371, p. 250-255, 6 pp. 2015

Publisher

Springer

Other information

Language

English

Type of outcome

Stať ve sborníku

Field of Study

10201 Computer sciences, information science, bioinformatics

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/00216224:14330/15:00080885

Organization unit

Faculty of Informatics

ISBN

978-3-319-25086-1

ISSN

DOI

http://dx.doi.org/10.1007/978-3-319-25087-8_24

UT WoS

000374289600024

Keywords in English

motion capture data; motion similarity; visualization; motion image; action classification

Abstract

V originále

The rapid development of motion capturing technologies has caused a massive usage of human motion data in a variety of fields, such as computer animation, gaming industry, medicine, sports and security. These technologies produce large volumes of complex spatio-temporal data which need to be effectively compared on the basis of similarity. In contrast to a traditional way of extracting numerical features, we propose a new idea to transform complex motion data into RGB images and compare them by content-based image retrieval methods. We see transformed RGB images as suitable application-independent features for their ability to preserve key aspects of performed motions. To demonstrate the usability of this idea, we evaluate a preliminary experiment that classifies 1,034 motions into 14 categories with the 87.4% precision.

Links

GBP103/12/G084, research and development project

Name: Centrum pro multi-modální interpretaci dat velkého rozsahu

Investor: Czech Science Foundation

Citovat

ELIÁŠ, Petr, Jan SEDMIDUBSKÝ and Pavel ZEZULA. Motion Images: An Effective Representation of Motion Capture Data for Similarity Search. In G. Amato et al. Proceedings of 8th International Conference on Similarity Search and Applications (SISAP 2015), LNCS 9371. Switzerland: Springer, 2015, p. 250-255. ISBN 978-3-319-25086-1. Available from: https://dx.doi.org/10.1007/978-3-319-25087-8_24.

@inproceedings{1305714,
   author = {Eliáš, Petr and Sedmidubský, Jan and Zezula, Pavel},
   address = {Switzerland},
   booktitle = {Proceedings of 8th International Conference on Similarity Search and Applications (SISAP 2015), LNCS 9371},
   doi = {http://dx.doi.org/10.1007/978-3-319-25087-8_24},
   editor = {G. Amato et al.},
   keywords = {motion capture data; motion similarity; visualization; motion image; action classification},
   howpublished = {tištěná verze "print"},
   language = {eng},
   location = {Switzerland},
   isbn = {978-3-319-25086-1},
   pages = {250-255},
   publisher = {Springer},
   title = {Motion Images: An Effective Representation of Motion Capture Data for Similarity Search},
   year = {2015}
}

TY  - JOUR
ID  - 1305714
AU  - Eliáš, Petr - Sedmidubský, Jan - Zezula, Pavel
PY  - 2015
TI  - Motion Images: An Effective Representation of Motion Capture Data for Similarity Search
PB  - Springer
CY  - Switzerland
SN  - 9783319250861
KW  - motion capture data
KW  - motion similarity
KW  - visualization
KW  - motion image
KW  - action classification
N2  - The rapid development of motion capturing technologies has caused a massive usage of human motion data in a variety of fields, such as computer animation, gaming industry, medicine, sports and security. These technologies produce large volumes of complex spatio-temporal data which need to be effectively compared on the basis of similarity. In contrast to a traditional way of extracting numerical features, we propose a new idea to transform complex motion data into RGB images and compare them by content-based image retrieval methods. We see transformed RGB images as suitable application-independent features for their ability to preserve key aspects of performed motions. To demonstrate the usability of this idea, we evaluate a preliminary experiment that classifies 1,034 motions into 14 categories with the 87.4% precision.
ER  -

ELIÁŠ, Petr, Jan SEDMIDUBSKÝ and Pavel ZEZULA. Motion Images: An Effective Representation of Motion Capture Data for Similarity Search. In G. Amato et al. \textit{Proceedings of 8th International Conference on Similarity Search and Applications (SISAP 2015), LNCS 9371}. Switzerland: Springer, 2015, p.~250-255. ISBN~978-3-319-25086-1. Available from: https://dx.doi.org/10.1007/978-3-319-25087-8\_{}24.

Detailed Information on Publication Record