Informační systém Repo
BALÁŽIA, Michal a Konstantinos N. PLATANIOTIS. Human Gait Recognition from Motion Capture Data in Signature Poses. IET Biometrics, London, UK: IET, 2017, roč. 6, č. 2, s. 129-137. ISSN 2047-4938.
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Základní údaje
Originální název Human Gait Recognition from Motion Capture Data in Signature Poses
Autoři BALÁŽIA, Michal (703 Slovensko, garant, domácí) a Konstantinos N. PLATANIOTIS (124 Kanada).
Vydání IET Biometrics, London, UK, IET, 2017, 2047-4938.
Další údaje
Originální jazyk angličtina
Typ výsledku Článek v odborném periodiku
Obor Informatika
Stát vydavatele Velká Británie
Utajení není předmětem státního či obchodního tajemství
WWW URL URL URL
Kód RIV RIV/00216224:14330/17:00095906
Organizace Fakulta informatiky - Masarykova univerzita
UT WoS 000396411600010
Klíčová slova česky rozpoznavani podle chuze
Klíčová slova anglicky gait recognition
Návaznosti MUNI/A/0915/2013. MUNI/A/1213/2014.
Změnil Změnil: RNDr. Daniel Jakubík, učo 139797. Změněno: 9. 8. 2018 00:55.
Anotace
Most contribution to the field of structure-based human gait recognition has been done through design of extraordinary gait features. Many research groups that address this topic introduce a unique combination of gait features, select a couple of well-known object classiers, and test some variations of their methods on their custom Kinect databases. For a practical system, it is not necessary to invent an ideal gait feature -- there have been many good geometric features designed -- but to smartly process the data there are at our disposal. This work proposes a gait recognition method without design of novel gait features; instead, we suggest an effective and highly efficient way of processing known types of features. Our method extracts a couple of joint angles from two signature poses within a gait cycle to form a gait pattern descriptor, and classifies the query subject by the baseline 1-NN classier. Not only are these poses distinctive enough, they also rarely accommodate motion irregularities that would result in confusion of identities. We experimentally demonstrate that our gait recognition method outperforms other relevant methods in terms of recognition rate and computational complexity. Evaluations were performed on an experimental database that precisely simulates street-level video surveillance environment.
Zobrazeno: 15. 7. 2020 00:13