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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2017

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of the 1st IAPR Workshop on Reproducible Research in Pattern Recognition (RRPR 2016)

ISBN

9783319564135

ISSN

0302-9743

e-ISSN

—

Počet stran výsledku

15

Strana od-do

33-47

Název nakladatele

Springer International Publishing AG

Místo vydání

Switzerland

Místo konání akce

Cancun, Mexico

Datum konání akce

1. 1. 2016

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000426089600003