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Title:
IDENTIFIKACIJA OSEB NA OSNOVI GIBANJA TELESA, ZAJETEGA Z NAPRAVO KINECT
Authors:
ID
Zlodej, Peter
(Author)
ID
Cigale, Boris
(Mentor)
More about this mentor...
ID
Šprager, Sebastijan
(Comentor)
Files:
UNI_Zlodej_Peter_2013.pdf
(1,92 MB)
MD5: E9F45BE6E78688B9E0B3D87413B7CE6B
Language:
Slovenian
Work type:
Bachelor thesis/paper
Typology:
2.11 - Undergraduate Thesis
Organization:
FERI - Faculty of Electrical Engineering and Computer Science
Abstract:
V diplomskem delu je predstavljen postopek identifikacije oseb na podlagi posnetkov človeške hoje, zajetih z napravo Kinect. Opisan je proces zajemanja signalov, predobdelave podatkov in strojnega učenja z metodo podpornih vektorjev. Predstavljeni so tudi rezultati testiranj uspešnosti postopka. Ugotovili smo, da je na podlagi signalov hoje, zajetih z napravo Kinect, možno razpoznati osebo iz množice petih oseb z natančnostjo 93,13 ± 9,99 %.
Keywords:
identifikacija oseb
,
senzor Kinect
,
analiza hoje
,
metoda podpornih vektorjev
,
biometrija.
Place of publishing:
Maribor
Publisher:
[P. Zlodej]
Year of publishing:
2013
PID:
20.500.12556/DKUM-42143
UDC:
004.8:004.932(043.2)
COBISS.SI-ID:
17526806
NUK URN:
URN:SI:UM:DK:G4J34A2I
Publication date in DKUM:
20.09.2013
Views:
2074
Downloads:
175
Metadata:
Categories:
KTFMB - FERI
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:
ZLODEJ, Peter, 2013,
IDENTIFIKACIJA OSEB NA OSNOVI GIBANJA TELESA, ZAJETEGA Z NAPRAVO KINECT
[online]. Bachelor’s thesis. Maribor : P. Zlodej. [Accessed 22 March 2025]. Retrieved from: https://dk.um.si/IzpisGradiva.php?lang=eng&id=42143
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Secondary language
Language:
English
Title:
IDENTIFICATION OF PERSONS USING FULL-BODY MOTION DATA CAPTURED WITH KINECT
Abstract:
This diploma presents a person identification method based on human gait signals, captured with Kinect sensor. It describes signal capture process, data preprocessing and machine learning with support vector machine. Test results of the proposed method are also presented in the document. Results show, that it is possible to recognize a person from a group of five persons, with accuracy of 93.13 ± 9.99 %, using human gait signals, captured with Kinect.
Keywords:
person identification
,
Kinect sensor
,
gait analysis
,
support vector machine
,
biometrics.
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