Walker-Independent Features for Gait Recognition from Motion
Capture Data

D 2016

Walker-Independent Features for Gait Recognition from Motion Capture Data

BALÁŽIA, Michal a Petr SOJKA

Základní údaje

Originální název

Walker-Independent Features for Gait Recognition from Motion Capture Data

Autoři

BALÁŽIA, Michal a Petr SOJKA

Vydání

LNCS 10029. Switzerland, Proceedings of the joint IAPR International Workshops on Structural and Syntactic Pattern Recognition (SSPR 2016) and Statistical Techniques in Pattern Recognition (SPR 2016), od s. 310-321, 12 s. 2016

Nakladatel

Springer International Publishing AG

Další údaje

Jazyk

angličtina

Typ výsledku

Stať ve sborníku

Obor

10201 Computer sciences, information science, bioinformatics

Stát vydavatele

Švýcarsko

Utajení

není předmětem státního či obchodního tajemství

Forma vydání

tištěná verze "print"

Odkazy

DOI, conference web

Impakt faktor

Impact factor: 0.402 v roce 2005

Kód RIV

RIV/00216224:14330/16:00090768

Organizační jednotka

Fakulta informatiky

ISBN

978-3-319-49054-0

ISSN

DOI

https://doi.org/10.1007/978-3-319-49055-7_28

UT WoS

000389509300028

EID Scopus

2-s2.0-84996761546

Klíčová slova česky

strojové učení; klasifikace; rozpoznávání podle chůze

Klíčová slova anglicky

machine learning; classification; gait recognition

Štítky

CLASSIFICATION, core_A, firank_A, gait recognition, machine learning

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 12. 2. 2018 17:11, RNDr. Michal Balážia, Ph.D.

Anotace

V originále

MoCap-based human identification, as a pattern recognition discipline, can be optimized using a machine learning approach. Yet in some applications such as video surveillance new identities can appear on the fly and labeled data for all encountered people may not always be available. This work introduces the concept of learning walker-independent gait features directly from raw joint coordinates by a modification of the Fisher’s Linear Discriminant Analysis with Maximum Margin Criterion. Our new approach shows not only that these features can discriminate different people than who they are learned on, but also that the number of learning identities can be much smaller than the number of walkers encountered in the real operation.

Návaznosti

MUNI/A/0892/2015, interní kód MU

Název: Výzkum v aplikované informatice na FI MU (Akronym: VAIFIMU)

Investor: Masarykova univerzita, Výzkum v aplikované informatice na FI MU, DO R. 2020_Kategorie A - Specifický výzkum - Studentské výzkumné projekty

MUNI/A/0935/2015, interní kód MU

Název: Zapojení studentů Fakulty informatiky do mezinárodní vědecké komunity (Akronym: SKOMU)

Investor: Masarykova univerzita, Zapojení studentů Fakulty informatiky do mezinárodní vědecké komunity, DO R. 2020_Kategorie A - Specifický výzkum - Studentské výzkumné projekty

Přiložené soubory

sspr10arxiv.pdf Verze souboru

Citovat

BALÁŽIA, Michal a Petr SOJKA. Walker-Independent Features for Gait Recognition from Motion Capture Data. In Antonio Robles-Kelly, Marco Loog, Battista Biggio, Francisco Escolano, Richard Wilson. Proceedings of the joint IAPR International Workshops on Structural and Syntactic Pattern Recognition (SSPR 2016) and Statistical Techniques in Pattern Recognition (SPR 2016). LNCS 10029. Switzerland: Springer International Publishing AG, 2016, s. 310-321. ISBN 978-3-319-49054-0. Dostupné z: https://doi.org/10.1007/978-3-319-49055-7_28.

@inproceedings{1353537,
   author = {Balážia, Michal and Sojka, Petr},
   address = {Switzerland},
   booktitle = {Proceedings of the joint IAPR International Workshops on Structural and Syntactic Pattern Recognition (SSPR 2016) and Statistical Techniques in Pattern Recognition (SPR 2016)},
   doi = {https://doi.org/10.1007/978-3-319-49055-7_28},
   edition = {LNCS 10029},
   editor = {Antonio Robles-Kelly, Marco Loog, Battista Biggio, Francisco Escolano, Richard Wilson},
   keywords = {machine learning; classification; gait recognition},
   howpublished = {tištěná verze "print"},
   language = {eng},
   location = {Switzerland},
   isbn = {978-3-319-49054-0},
   pages = {310-321},
   publisher = {Springer International Publishing AG},
   title = {Walker-Independent Features for Gait Recognition from Motion Capture Data},
   url = {https://doi.org/10.1007/978-3-319-49055-7_28},
   year = {2016}
}

TY  - CONF
ID  - 1353537
AU  - Balážia, Michal - Sojka, Petr
PY  - 2016
TI  - Walker-Independent Features for Gait Recognition from Motion Capture Data
PB  - Springer International Publishing AG
CY  - Switzerland
SN  - 9783319490540
KW  - machine learning
KW  - classification
KW  - gait recognition
UR  - https://doi.org/10.1007/978-3-319-49055-7_28
L2  - https://doi.org/10.1007/978-3-319-49055-7_28
N2  - MoCap-based human identification, as a pattern recognition discipline, can be optimized using a machine learning approach. Yet in some applications such as video surveillance new identities can appear on the fly and labeled data for all encountered people may not always be available. This work introduces the concept of learning walker-independent gait features directly from raw joint coordinates by a modification of the Fisher’s Linear Discriminant Analysis with Maximum Margin Criterion. Our new approach shows not only that these features can discriminate different people than who they are learned on, but also that the number of learning identities can be much smaller than the number of walkers encountered in the real operation.
ER  -

BALÁŽIA, Michal a Petr SOJKA. Walker-Independent Features for Gait Recognition from Motion Capture Data. In Antonio Robles-Kelly, Marco Loog, Battista Biggio, Francisco Escolano, Richard Wilson. \textit{Proceedings of the joint IAPR International Workshops on Structural and Syntactic Pattern Recognition (SSPR 2016) and Statistical Techniques in Pattern Recognition (SPR 2016)}. LNCS 10029. Switzerland: Springer International Publishing AG, 2016, s.~310-321. ISBN~978-3-319-49054-0. Dostupné z: https://doi.org/10.1007/978-3-319-49055-7\_{}28.

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