Araştırma Makalesi
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Gerçek Zamanlı Sürücü Yorgunluk Tespit Sistemi

Yıl 2018, Cilt: 30 Sayı: 3, 249 - 259, 30.09.2018
https://doi.org/10.7240/marufbd.417915

Öz

Bu çalışmada,
görüntü işleme tabanlı sürücü yorgunluk tespit sistemi ile yorgunluk ve
uykusuzluğun yol açtığı trafik kazalarının önüne geçilmesi amaçlanmıştır.
Geliştirilen sistem, farklı aydınlık seviyelerde sürücünün göz hareketlerini
kameradan anlık olarak izlemekte, analiz etmekte ve gerekli durumda alarm
vermektedir. Yorgunluk tespiti yapılırken PERCLOS (Percentage of Eye Closure)
metriği kullanılmıştır. PERCLOS metriği tespit edilen gözlerin eşik değerler
baz alınarak çevrilmiş binary görüntülerindeki piksel sayımı yapılıp ardından
önceden hesaplanmış averaj değeri ile kıyaslanması sonucu gözlerin kapalı veya
açık olduğuna karar verilmesi işlemlerine dayanmaktadır. Sürücüde yorgunluk
tespiti yapıldığı anda Raspberry Pi 3 gömülü sistemi üzerinden alarm sisteminin
devreye girmesi ve kablosuz haberleşme yardımı ile önceden belirlenmiş bir
hesaba durum hakkında görüntülü ve yazılı bildirim yapılması sağlanmıştır. 

Kaynakça

  • Liang, Y., Reyes, M.L., Lee, J.D. 2007. “Real-time detection of driver cognitive distraction using support vector machines”, IEEE Transact. Intell. Transport. Syst., 8, 340–350.
  • Chen, Y.L., Chiang, H. H., Chiang, C.Y., Liu, C.M., Yuan, S.M., Wang, J.H. 2012. “A vision-based driver nighttime assistance and surveillance system based on intelligent image sensing techniques and a heterogamous dual-core embedded system architecture”, Sensors, 12, 2373–2399.
  • Lee, B.G., Chung, W.Y.A. 2012. “Smartphone-based driver safety monitoring system using data fusion”, Sensors, 12, 17536–17552.
  • Lenskiy, A.A., Lee, J. 2012. “Driver’s eye blinking detection using novel color and texture segmentation algorithms”, International Journal of Control, Automation and Systems, 10, 317-327.
  • Liang, Y., Lee J.D. 2014. “A hybrid Bayesian Network approach to detect driver cognitive distraction”,Transport. Res. Part C: Emerg. Technol., 38, 146–155.
  • Masala, G.L., Grosso E. 2014. “Real time detection of driver attention: Emerging solutions based on robust iconic classifiers and dictionary of poses”, Transportation Research Part C, 49, 32-42.
  • Ghosh, S., Nandy, T., Manna, N. 2015. ”Real Time Eye Detection and Tracking Method for Driver Assistance System”, Advancements of Medical Electronics, Lecture Notes in Bioengineering, 13-25.
  • Cyganek, B. 2016. “Real-Time Eye Detection and Tracking in the Near-Infrared Video for Drivers’ Drowsiness Control”, Proceedings of the 9th International Conference on Computer Recognition Systems CORES 2015, Advances in Intelligent Systems and Computing, 403, 481-490.
  • Takahashi, K. 2005. "Method of detecting concentration on cellular phone call from facial expression change by image processing", IEEE International Conference on Systems, Man and Cybernetics, 4, 3444-3448.
  • Zhang, X., Zheng, N., Wang, F., He. Y. 2011. “Visual Recognition of Driver Hand-held Cell Phone Use Based on Hidden CRF” IEEE International Conference on Vehicular Electronics and Safety (ICVES), 248–251.
  • Wang, D., Pei, M., Zhu, L. 2014. “Detecting Driver Use of Mobile Phone Based on In-Car Camera”, 10th International Conference on Computational Intelligence and Security, 148-151.
  • Ahmed,R., Emon, K.E.K., Hossain,M.F. .2014. “Robust Driver Fatigue Recognition Using Image Processing”, 3rd Int. IEEE Conference on Informatics, Electronics & Vision,1-6.
  • Beukman, A.R., Hancke, G.P., Silva, B.J. 2016, “A multi-sensor system for detection of driver fatigue”, Industrial Informatics IEEE 14th International Conference on (INDIN), South Africa
  • Fitriyani, N.L., Yang, C.K., Syafrudin, M. 2016. “Real-Time Eye State Detection System Using Haar Cascade Classifier and Circular Hough Transform”, IEEE 5th Global Conference on Consumer Electronics, Japan
  • Yan,J.J., Kuo,H.H., Lin,Y.F., Liao, T.L. 2016. “Real-time Driver Drowsiness Detection System Based on PERCLOS and Grayscale Image Processing”, International Symposium on Computer, Consumer and Control (IS3C), China
  • Acıoğlu, A., Erçelebi, E. 2016. “Real Time Eye Detection Algorithm for PERCLOS Calculation”, 24th Signal Processing and Communication Application Conference (SIU), Turkey
  • Porwik, P., Lisowska, A. 2004. “The Haar–Wavelet Transform in Digital Image Processing: Its Status and Achievements”, Machine Graphics & Vision, 13, 79-98.
  • Viola, P., Jones, M. 2001. “Rapid Object Detection using a Boosted Cascade of Simple Features”, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 511-518.
  • Dalal, N., Triggs B. 2005. “Histograms of oriented gradients for human detection”, Proceedings on IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1, 886-893.
  • Ahonen, T., Hadid, A., Pietikäinen, M. 2004. "Face Recognition with Local Binary Patterns," Proc. Eighth European Conf. Computer Vision, 469-481.
  • Ojala, T., Pietikäinen, M., Harwood, D. 1994, "Performance evaluation of texture measures with classification based on Kullback discrimination of distributions", Proceedings of the 12th IAPR International Conference on Pattern Recognition (ICPR 1994), vol. 1, 582 - 585.
Yıl 2018, Cilt: 30 Sayı: 3, 249 - 259, 30.09.2018
https://doi.org/10.7240/marufbd.417915

Öz

Kaynakça

  • Liang, Y., Reyes, M.L., Lee, J.D. 2007. “Real-time detection of driver cognitive distraction using support vector machines”, IEEE Transact. Intell. Transport. Syst., 8, 340–350.
  • Chen, Y.L., Chiang, H. H., Chiang, C.Y., Liu, C.M., Yuan, S.M., Wang, J.H. 2012. “A vision-based driver nighttime assistance and surveillance system based on intelligent image sensing techniques and a heterogamous dual-core embedded system architecture”, Sensors, 12, 2373–2399.
  • Lee, B.G., Chung, W.Y.A. 2012. “Smartphone-based driver safety monitoring system using data fusion”, Sensors, 12, 17536–17552.
  • Lenskiy, A.A., Lee, J. 2012. “Driver’s eye blinking detection using novel color and texture segmentation algorithms”, International Journal of Control, Automation and Systems, 10, 317-327.
  • Liang, Y., Lee J.D. 2014. “A hybrid Bayesian Network approach to detect driver cognitive distraction”,Transport. Res. Part C: Emerg. Technol., 38, 146–155.
  • Masala, G.L., Grosso E. 2014. “Real time detection of driver attention: Emerging solutions based on robust iconic classifiers and dictionary of poses”, Transportation Research Part C, 49, 32-42.
  • Ghosh, S., Nandy, T., Manna, N. 2015. ”Real Time Eye Detection and Tracking Method for Driver Assistance System”, Advancements of Medical Electronics, Lecture Notes in Bioengineering, 13-25.
  • Cyganek, B. 2016. “Real-Time Eye Detection and Tracking in the Near-Infrared Video for Drivers’ Drowsiness Control”, Proceedings of the 9th International Conference on Computer Recognition Systems CORES 2015, Advances in Intelligent Systems and Computing, 403, 481-490.
  • Takahashi, K. 2005. "Method of detecting concentration on cellular phone call from facial expression change by image processing", IEEE International Conference on Systems, Man and Cybernetics, 4, 3444-3448.
  • Zhang, X., Zheng, N., Wang, F., He. Y. 2011. “Visual Recognition of Driver Hand-held Cell Phone Use Based on Hidden CRF” IEEE International Conference on Vehicular Electronics and Safety (ICVES), 248–251.
  • Wang, D., Pei, M., Zhu, L. 2014. “Detecting Driver Use of Mobile Phone Based on In-Car Camera”, 10th International Conference on Computational Intelligence and Security, 148-151.
  • Ahmed,R., Emon, K.E.K., Hossain,M.F. .2014. “Robust Driver Fatigue Recognition Using Image Processing”, 3rd Int. IEEE Conference on Informatics, Electronics & Vision,1-6.
  • Beukman, A.R., Hancke, G.P., Silva, B.J. 2016, “A multi-sensor system for detection of driver fatigue”, Industrial Informatics IEEE 14th International Conference on (INDIN), South Africa
  • Fitriyani, N.L., Yang, C.K., Syafrudin, M. 2016. “Real-Time Eye State Detection System Using Haar Cascade Classifier and Circular Hough Transform”, IEEE 5th Global Conference on Consumer Electronics, Japan
  • Yan,J.J., Kuo,H.H., Lin,Y.F., Liao, T.L. 2016. “Real-time Driver Drowsiness Detection System Based on PERCLOS and Grayscale Image Processing”, International Symposium on Computer, Consumer and Control (IS3C), China
  • Acıoğlu, A., Erçelebi, E. 2016. “Real Time Eye Detection Algorithm for PERCLOS Calculation”, 24th Signal Processing and Communication Application Conference (SIU), Turkey
  • Porwik, P., Lisowska, A. 2004. “The Haar–Wavelet Transform in Digital Image Processing: Its Status and Achievements”, Machine Graphics & Vision, 13, 79-98.
  • Viola, P., Jones, M. 2001. “Rapid Object Detection using a Boosted Cascade of Simple Features”, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 511-518.
  • Dalal, N., Triggs B. 2005. “Histograms of oriented gradients for human detection”, Proceedings on IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1, 886-893.
  • Ahonen, T., Hadid, A., Pietikäinen, M. 2004. "Face Recognition with Local Binary Patterns," Proc. Eighth European Conf. Computer Vision, 469-481.
  • Ojala, T., Pietikäinen, M., Harwood, D. 1994, "Performance evaluation of texture measures with classification based on Kullback discrimination of distributions", Proceedings of the 12th IAPR International Conference on Pattern Recognition (ICPR 1994), vol. 1, 582 - 585.
Toplam 21 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Mühendislik
Bölüm Araştırma Makaleleri
Yazarlar

Revna Acar Vural 0000-0002-8587-5185

Mustafa Yiğit Sert Bu kişi benim 0000-0001-6498-4027

Büşra Karaköse Bu kişi benim 0000-0002-8781-3113

Yayımlanma Tarihi 30 Eylül 2018
Kabul Tarihi 3 Eylül 2018
Yayımlandığı Sayı Yıl 2018 Cilt: 30 Sayı: 3

Kaynak Göster

APA Acar Vural, R., Sert, M. Y., & Karaköse, B. (2018). Gerçek Zamanlı Sürücü Yorgunluk Tespit Sistemi. Marmara Fen Bilimleri Dergisi, 30(3), 249-259. https://doi.org/10.7240/marufbd.417915
AMA Acar Vural R, Sert MY, Karaköse B. Gerçek Zamanlı Sürücü Yorgunluk Tespit Sistemi. MFBD. Eylül 2018;30(3):249-259. doi:10.7240/marufbd.417915
Chicago Acar Vural, Revna, Mustafa Yiğit Sert, ve Büşra Karaköse. “Gerçek Zamanlı Sürücü Yorgunluk Tespit Sistemi”. Marmara Fen Bilimleri Dergisi 30, sy. 3 (Eylül 2018): 249-59. https://doi.org/10.7240/marufbd.417915.
EndNote Acar Vural R, Sert MY, Karaköse B (01 Eylül 2018) Gerçek Zamanlı Sürücü Yorgunluk Tespit Sistemi. Marmara Fen Bilimleri Dergisi 30 3 249–259.
IEEE R. Acar Vural, M. Y. Sert, ve B. Karaköse, “Gerçek Zamanlı Sürücü Yorgunluk Tespit Sistemi”, MFBD, c. 30, sy. 3, ss. 249–259, 2018, doi: 10.7240/marufbd.417915.
ISNAD Acar Vural, Revna vd. “Gerçek Zamanlı Sürücü Yorgunluk Tespit Sistemi”. Marmara Fen Bilimleri Dergisi 30/3 (Eylül 2018), 249-259. https://doi.org/10.7240/marufbd.417915.
JAMA Acar Vural R, Sert MY, Karaköse B. Gerçek Zamanlı Sürücü Yorgunluk Tespit Sistemi. MFBD. 2018;30:249–259.
MLA Acar Vural, Revna vd. “Gerçek Zamanlı Sürücü Yorgunluk Tespit Sistemi”. Marmara Fen Bilimleri Dergisi, c. 30, sy. 3, 2018, ss. 249-5, doi:10.7240/marufbd.417915.
Vancouver Acar Vural R, Sert MY, Karaköse B. Gerçek Zamanlı Sürücü Yorgunluk Tespit Sistemi. MFBD. 2018;30(3):249-5.

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