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@inproceedings{23247,
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)},
edition = {LNCS 10029},
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 - JOUR
ID - 23247
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
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 \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. s.~310-321. ISBN~978-3-319-49054-0. 2016.
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