2018
Gait Recognition from Motion Capture Data
BALÁŽIA, Michal a Petr SOJKAZákladní údaje
Originální název
Gait Recognition from Motion Capture Data
Autoři
BALÁŽIA, Michal (703 Slovensko, garant, domácí) a Petr SOJKA (203 Česká republika, domácí)
Vydání
ACM Transactions on Multimedia Computing, Communications and Applications (TOMM), special issue on Representation, Analysis and Recognition of 3D Humans, New York, USA, ACM, 2018, 1551-6857
Další údaje
Jazyk
angličtina
Typ výsledku
Článek v odborném periodiku
Obor
Informatika
Stát vydavatele
Spojené státy
Utajení
není předmětem státního či obchodního tajemství
Kód RIV
RIV/00216224:14330/18:00102051
Organizace
Fakulta informatiky – Masarykova univerzita – Repozitář
UT WoS
000433517100008
EID Scopus
2-s2.0-85042907000
Klíčová slova česky
rozpoznávání podle chůze
Klíčová slova anglicky
gait recognition
Návaznosti
MUNI/A/0992/2016, interní kód Repo. MUNI/A/0997/2016, interní kód Repo.
Změněno: 1. 10. 2020 01:51, RNDr. Daniel Jakubík
Anotace
V originále
Gait recognition from motion capture data, as a pattern classification discipline, can be improved by the use of machine learning. This paper contributes to the state-of-the-art with a statistical approach for extracting robust gait features directly from raw data by a modification of Linear Discriminant Analysis with Maximum Margin Criterion. Experiments on the CMU MoCap database show that the suggested method outperforms thirteen relevant methods based on geometric features and a method to learn the features by a combination of Principal Component Analysis and Linear Discriminant Analysis. The methods are evaluated in terms of the distribution of biometric templates in respective feature spaces expressed in a number of class separability coefficients and classification metrics. Results also indicate a high portability of learned features, that means, we can learn what aspects of walk people generally differ in and extract those as general gait features. Recognizing people without needing group-specific features is convenient as particular people might not always provide annotated learning data. As a contribution to reproducible research, our evaluation framework and database have been made publicly available. This research makes motion capture technology directly applicable for human recognition.