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 a Pavel ZEZULA

Základní údaje

Originální název

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

Autoři

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

Vydání

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

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"

Impakt faktor

Impact factor: 0.402 v roce 2005

Kód RIV

RIV/00216224:14330/15:00080885

Organizační jednotka

Fakulta informatiky

ISBN

978-3-319-25086-1

ISSN

DOI

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

UT WoS

000374289600024

Klíčová slova anglicky

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

Štítky

DISA

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 24. 1. 2018 16:23, RNDr. Petr Eliáš, Ph.D.

Anotace

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.

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

ELIÁŠ, Petr, Jan SEDMIDUBSKÝ a 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, s. 250-255. ISBN 978-3-319-25086-1. Dostupné z: 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 a 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, s.~250-255. ISBN~978-3-319-25086-1. Dostupné z: https://dx.doi.org/10.1007/978-3-319-25087-8\_{}24.

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