An Evaluation Framework and Database for MoCap-Based Gait Recognition Methods
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F17%3A00095907" target="_blank" >RIV/00216224:14330/17:00095907 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1007/978-3-319-56414-2_3" target="_blank" >https://doi.org/10.1007/978-3-319-56414-2_3</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-319-56414-2_3" target="_blank" >10.1007/978-3-319-56414-2_3</a>
Alternative languages
Result language
angličtina
Original language name
An Evaluation Framework and Database for MoCap-Based Gait Recognition Methods
Original language description
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.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2017
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of the 1st IAPR Workshop on Reproducible Research in Pattern Recognition (RRPR 2016)
ISBN
9783319564135
ISSN
0302-9743
e-ISSN
—
Number of pages
15
Pages from-to
33-47
Publisher name
Springer International Publishing AG
Place of publication
Switzerland
Event location
Cancun, Mexico
Event date
Jan 1, 2016
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000426089600003