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@inproceedings{1359622,
author = {Balážia, Michal and Sojka, Petr},
address = {Switzerland},
booktitle = {Proceedings of the 1st IAPR Workshop on Reproducible Research in Pattern Recognition (RRPR 2016)},
doi = {http://dx.doi.org/10.1007/978-3-319-56414-2_3},
edition = {LNCS 10214},
editor = {Miguel Colom, Bertrand Kerautret and Pascal Monasse},
keywords = {software evaluation framework; gait cycle database; human gait recognition},
howpublished = {tištěná verze "print"},
language = {eng},
location = {Switzerland},
isbn = {978-3-319-56413-5},
pages = {33-47},
publisher = {Springer International Publishing AG},
title = {An Evaluation Framework and Database for MoCap-Based Gait Recognition Methods},
url = {https://doi.org/10.1007/978-3-319-56414-2_3},
year = {2017}
}
TY - JOUR
ID - 1359622
AU - Balážia, Michal - Sojka, Petr
PY - 2017
TI - An Evaluation Framework and Database for MoCap-Based Gait Recognition Methods
PB - Springer International Publishing AG
CY - Switzerland
SN - 9783319564135
KW - software evaluation framework
KW - gait cycle database
KW - human gait recognition
UR - https://doi.org/10.1007/978-3-319-56414-2_3
L2 - https://doi.org/10.1007/978-3-319-56414-2_3
N2 - As a contribution to reproducible research, this paper presents a framework and a database to improve the development, evaluation and comparison of methods for gait recognition from Motion Capture (MoCap) data. The evaluation framework provides implementation details and source codes of state-of-the-art human-interpretable geometric features as well as our own approaches where gait features are learned by a modification of Fisher's Linear Discriminant Analysis with the Maximum Margin Criterion, and by a combination of Principal Component Analysis and Linear Discriminant Analysis. It includes a description and source codes of a mechanism for evaluating four class separability coefficients of feature space and four rank-based classifier performance metrics. This framework also contains a tool for learning a custom classifier and for classifying a custom query on a custom gallery. We provide an experimental database along with source codes for its extraction from the general CMU MoCap database.
ER -
BALÁŽIA, Michal and Petr SOJKA. An Evaluation Framework and Database for MoCap-Based Gait Recognition Methods. In Miguel Colom, Bertrand Kerautret and Pascal Monasse. \textit{Proceedings of the 1st IAPR Workshop on Reproducible Research in Pattern Recognition (RRPR 2016)}. LNCS 10214. Switzerland: Springer International Publishing AG, 2017, p.~33-47. ISBN~978-3-319-56413-5. Available from: https://dx.doi.org/10.1007/978-3-319-56414-2\_{}3.
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