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An Evaluation Framework and Database for MoCap-Based Gait Recognition Methods

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BALÁŽIA, Michal and Petr SOJKA. An Evaluation Framework and Database for MoCap-Based Gait Recognition Methods. In 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, 15 pp. ISBN 978-3-319-56413-5.
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Basic information
Original name An Evaluation Framework and Database for MoCap-Based Gait Recognition Methods
Authors BALÁŽIA, Michal (703 Slovakia, guarantor, belonging to the institution) and Petr SOJKA (203 Czech Republic, belonging to the institution).
Edition LNCS 10214. Switzerland, Proceedings of the 1st IAPR Workshop on Reproducible Research in Pattern Recognition (RRPR 2016), p. 33-47, 15 pp. 2017.
Publisher Springer International Publishing AG
Other information
Original language English
Type of outcome Proceedings paper
Field of Study Informatics
Country of publisher Switzerland
Confidentiality degree is not subject to a state or trade secret
Publication form printed version "print"
WWW URL URL URL
RIV identification code RIV/00216224:14330/17:00095907
Organization Fakulta informatiky - Repository
ISBN 978-3-319-56413-5
ISSN 0302-9743
UT WoS 000426089600003
Keywords (in Czech) softwarový evaluační framework; databáze dvoukroků; rozpoznávání lidi podle chůze
Keywords in English software evaluation framework; gait cycle database; human gait recognition
Links MUNI/A/0892/2015. MUNI/A/0935/2015.
Changed by Changed by: RNDr. Daniel Jakubík, učo 139797. Changed: 19. 5. 2018 00:54.
Abstract
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.
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000357_rrpr2arxiv_devdnils.pdf   File version 26. 9. 2017

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rrpr-2016-evaluation.pdf Licence Creative Commons  File version 9. 11. 2016

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  • a concrete person RNDr. Daniel Jakubík, uco 139797
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