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AKILLI SİSTEMLER VE UYGULAMALARI DERGİSİ
JOURNAL OF INTELLIGENT SYSTEMS WITH APPLICATIONS
J. Intell. Syst. Appl.
E-ISSN: 2667-6893
Creative Commons License This work is licensed under a Creative Commons Attribution 4.0 International License.

Development of Driver Fatigue Detection System By Using Video Images

Video Görüntüleri Kullanılarak Sürücü Yorgunluğu Sezme Sistemi Geliştirilmesi

How to cite: Kır Savaş B, Becerikli Y. Development of driver fatigue detection system by using video images. Akıllı Sistemler ve Uygulamaları Dergisi (Journal of Intelligent Systems with Applications) 2019; 2(1): 26-29. DOI: 10.54856/jiswa.201905054

Full Text: PDF, in Turkish.

Total number of downloads: 579

Title: Development of Driver Fatigue Detection System By Using Video Images

Abstract: Major reasons for traffic accidents all over the world are mostly because of drivers' fatigue and lack of concentration. In this study, the detection and tracking of the drivers' faces in video based images were realized by using AdaBoost algorithm. The eye area was detected by using Principle Component Analysis (PCA). A predictive system was developed analyzing the eye closure of the drivers'. The system used PERCLOS (Percentage of eye closure) and it was tested on UCLA database.

Keywords: Face detection; eye detection; drivers' fatigue detection; AdaBoost algorithm; PCA algorithm


Başlık: Video Görüntüleri Kullanılarak Sürücü Yorgunluğu Sezme Sistemi Geliştirilmesi

Özet: Değişik sebeplerle sürücülerde meydana gelen yorgunluk, uyuşukluk ve dikkatsizlik tüm dünyada trafik kazalarının başlıca sebeplerindendir. Bu çalışmada video tabanlı görüntülerde sürücü yüz bölgelerinin tespiti ve takibi AdaBoost algoritması kullanılarak yapılmıştır. Elde edilen yüz bölgelerinde Temel Bileşen Analizi (TBA) algoritması kullanılarak göz bölgesi tespit edilmiştir. Göz bölgesinin açıklık/kapalılık durumuna bakılarak sürücülerin yorgun olup olmadıklarını sezen ve yorgunluğa dair tahminde bulunan bir sistem geliştirilmiştir. Gözün kapalılık durumu için PERCLOS'dan yararlanılmıştır. Uygulamayı test etmek için UCLA veritabanı kullanılmıştır.

Anahtar kelimeler: Yüz tespiti; göz tespiti; sürücü yorgunluğu tespiti; Adaboost algoritması; TBA algoritması


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