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master's thesis
Detekcija umora vozača iz video snimki
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2017. urn:nbn:hr:168:371455
Omanović, Tarik
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

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Omanović, T. (2017). Detekcija umora vozača iz video snimki (Master's thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:371455

Omanović, Tarik. "Detekcija umora vozača iz video snimki." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2017. https://urn.nsk.hr/urn:nbn:hr:168:371455

Omanović, Tarik. "Detekcija umora vozača iz video snimki." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2017. https://urn.nsk.hr/urn:nbn:hr:168:371455

Omanović, T. (2017). 'Detekcija umora vozača iz video snimki', Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 27 December 2024, https://urn.nsk.hr/urn:nbn:hr:168:371455

Omanović T. Detekcija umora vozača iz video snimki [Master's thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2017 [cited 2024 December 27] Available at: https://urn.nsk.hr/urn:nbn:hr:168:371455

T. Omanović, "Detekcija umora vozača iz video snimki", Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2017. Available at: https://urn.nsk.hr/urn:nbn:hr:168:371455

Metadata
TitleDetekcija umora vozača iz video snimki
Title (english)Driver Fatigue Monitoring from Video Data
AuthorTarik Omanović
MentorSven Lončarić (mentor)
Committee memberSven Lončarić (predsjednik povjerenstva)
Committee memberMarko Subašić (član povjerenstva)
Committee memberTomislav Petković (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2017-07-14, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Vožnja u umornom stanju predstavlja veliku opasnost, stoga postoji realna potreba za detekcijom umora vozača. Detekcija umora iz video signala predstavlja primjenjiv neinvazivan pristup tom problemu. Video signal analiziran je uz pomoć komercijalnog alata FaceTrack proizvođača Visage Technologies te su dobivene značajke lica podijeljene u tri kategorije: jednostavne, složene i one definirane vremenom. Napravljene su video snimke umornih vozača, te je na njima ručno očitana razina umora u svakom trenutku. Na video snimkama provedeno je četiri algoritma strojnog učenja. To su k-NN (k najbližih susjeda), SVM (stroj potpornih vektora), AdaBoost i Random forest (nasumična šuma). Svi algoritmi su trenirani na svim skupovima značajki, te su dobiveni ohrabrujući rezultati.
Abstract (english)
Drowsy driving poses a threat to public safety, so there is a real need for drowsiness detection. Drowsiness detection from video signal presents a practical, non intrusive approach to this issue. Video signal is analyzed using commercial tool FaceTrack from Visage Technologies. Calculated facial features are divided in three categories: simple features, complex features and features defined by time. Several drowsy individuals have been recorded and level of drowsiness has been manually marked on each of them. Four machine learning algorithms have been trained on video signal. Those are k nearest neighbors, SVM, AdaBoost and Random forest. All algorithms have been tested on all sets of features with encouraging results.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:371455
Study programmeTitle: Computing
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra inženjer/inženjerka računarstva (magistar/magistra inženjer/inženjerka računarstva)
Type of resourceText
File originBorn digital
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Created on2019-03-13 23:29:59