Research Article
Year 2023,
, 718 - 728, 21.08.2023
Abstract
Uykululuk halinin önceden tespit edilmesi, uykululuğa bağlı trafik kazalarını önlemek,
fiziksel ve ekonomik kayıpların önüne geçmek açısından önemlidir. Bir kamera yardımı
ile kişilerin görüntüleri üzerinde görüntü işleme teknikleri uygulanarak uykululuk halini
önceden kestirmek mümkündür. Bu çalışmada, literatürdeki veri kümelerinden
yararlanılarak uykululuk halinin tespit edilmesinde büyük bilgiye sahip olan göz
bölgesinden gelen öznitelikler ile göz kırpma tespiti yapmak için sabit ve uyarlamalı eşik
değerleri ayrı ayrı değerlendirilmiştir. Böylece, kısa süreli göz kırpma ile uzun süreli göz
kapamanın daha iyi ayırt edilmesi hedeflenmiştir. Çalışmada önerilen uyarlamalı eşik
değerinin sabit bir eşik değerinden çok daha başarılı göz kırpma tespiti sonuçları verdiği,
iki farklı veri kümesi üzerinde yapılan deneyler ile doğrulanmıştır.
References
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- Belge, E. ve Yildiz, A. (2018). Identıfıcatıon of Driver Doziness as Real Time With Image Processing Technigue And Warning of Driver. 9th International Automotive Technologies Congress, OTEKON 2018, 1400–1409.
- Cech, J. ve Soukupova, T. (2016). Real-Time Eye Blink Detection using Facial Landmarks. Center for Machine Perception, Department of Cybernetics Faculty of Electrical Engineering, Czech Technical University in Prague, 1–8.
- Drutarovsky, T. ve Fogelton, A. (2015). Eye blink detection using variance of motion vectors. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) içinde (C. 8927, ss. 436–448). doi:10.1007/978-3-319-16199-0_31
- Eyeblink - Research. (2019). 07 Eylül 2022 tarihinde https://www.blinkingmatters.com/research adresinden erişildi.
- Friedrichs, F. ve Yang, B. (2010). Camera-based drowsiness reference for driver state classification under real driving conditions. IEEE Intelligent Vehicles Symposium, Proceedings içinde (ss. 101–106). doi:10.1109/IVS.2010.5548039
- Ghoddoosian, R., Galib, M. ve Athitsos, V. (2019). A realistic dataset and baseline temporal model for early drowsiness detection. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2019-June, 178–187. doi:10.1109/CVPRW.2019.00027
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- King, D. E. (2009). Dlib-ml: A machine learning toolkit. Journal of Machine Learning Research, 10, 1755–1758.
- Li, X., Pfister, T., Huang, X., Zhao, G. ve Pietikainen, M. (2013). A Spontaneous Micro-expression Database: Inducement, collection and baseline. 2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition, FG 2013 içinde . doi:10.1109/FG.2013.6553717
- Massoz, Q., Langohr, T., Francois, C. ve Verly, J. G. (2016). The ULg multimodality drowsiness database (called DROZY) and examples of use. 2016 IEEE Winter Conference on Applications of Computer Vision, WACV 2016 içinde . doi:10.1109/WACV.2016.7477715
- McIntire, L. K., McKinley, R. A., Goodyear, C. ve McIntire, J. P. (2014). Detection of vigilance performance using eye blinks. Applied Ergonomics, 45(2 PB), 354–362. doi:10.1016/j.apergo.2013.04.020
- Reddy, B., Kim, Y. H., Yun, S., Seo, C. ve Jang, J. (2017). Real-Time Driver Drowsiness Detection for Embedded System Using Model Compression of Deep Neural Networks. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2017-July, 438–445. doi:10.1109/CVPRW.2017.59
- Sahayadhas, A., Sundaraj, K. ve Murugappan, M. (2012). Detecting driver drowsiness based on sensors: A review. Sensors (Switzerland), 12(12), 16937–16953. doi:10.3390/s121216937
- Soleimanloo, S. S., Wilkinson, V. E., Cori, J. M., Westlake, J., Stevens, B., Downey, L. A., … Howard, M. E. (2019). Eye-blink parameters detect on-road track-driving impairment following severe sleep deprivation. Journal of Clinical Sleep Medicine, 15(9), 1271–1284. doi:10.5664/jcsm.7918
- Suzuki, M., Yamamoto, N., Yamamoto, O., Nakano, T. ve Yamamoto, S. (2006). Measurement of driver’s consciousness by image processing - A method for presuming driver’s drowsiness by eye-blinks coping with individual differences. Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics içinde (C. 4, ss. 2891–2896). doi:10.1109/ICSMC.2006.385313
- Svensson, U. (2004). Blink Behaviour Based Drowsiness Detection: Method Development and Validation. (Applied Physics and Electrical Engineering, MSc). Linköping University
- Weng, C. H., Lai, Y. H. ve Lai, S. H. (2017). Driver drowsiness detection via a hierarchical temporal deep belief network. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) içinde (C. 10118 LNCS, ss. 117–133). Springer, Cham. doi:10.1007/978-3-319-54526-4_9
- Yan, W. J., Li, X., Wang, S. J., Zhao, G., Liu, Y. J., Chen, Y. H. ve Fu, X. (2014). CASME II: An improved spontaneous micro-expression database and the baseline evaluation. PLoS ONE, 9(1). doi:10.1371/journal.pone.0086041
- Yan, W. J., Wu, Q., Liu, Y. J., Wang, S. J. ve Fu, X. (2013). CASME database: A dataset of spontaneous micro-expressions collected from neutralized faces. 2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition, FG 2013 içinde . doi:10.1109/FG.2013.6553799
Year 2023,
, 718 - 728, 21.08.2023
Abstract
Detecting drowsiness in advance is very important for preventing traffic possible
accidents due to fatigue which result in physical and economic losses. It is possible to
predict drowsiness by applying computer vision techniques to facial videos captures
using a camera. In this study, the features from the eye region, which carry a lot of
information for drowsiness detection were evaluated for eye-blink detection with fixed
and adaptive thresholds using the datasets in the literature. The goal was to better
discriminate short eye-blinks and longer eye closures. It was experimentally confirmed
on two different datasets confirmed that the proposed adaptive thresholding method
gives more accurate eye-blink detection results as compared to the fixed threshold.
References
- Amato, G., Falchi, F., Gennaro, C. ve Vairo, C. (2018). A comparison of face verification with facial landmarks and deep features. Proceedings of the 10th International Conference on Advances in Multimedia (MMEDIA 2018), (c), 1–6.
- Belge, E. ve Yildiz, A. (2018). Identıfıcatıon of Driver Doziness as Real Time With Image Processing Technigue And Warning of Driver. 9th International Automotive Technologies Congress, OTEKON 2018, 1400–1409.
- Cech, J. ve Soukupova, T. (2016). Real-Time Eye Blink Detection using Facial Landmarks. Center for Machine Perception, Department of Cybernetics Faculty of Electrical Engineering, Czech Technical University in Prague, 1–8.
- Drutarovsky, T. ve Fogelton, A. (2015). Eye blink detection using variance of motion vectors. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) içinde (C. 8927, ss. 436–448). doi:10.1007/978-3-319-16199-0_31
- Eyeblink - Research. (2019). 07 Eylül 2022 tarihinde https://www.blinkingmatters.com/research adresinden erişildi.
- Friedrichs, F. ve Yang, B. (2010). Camera-based drowsiness reference for driver state classification under real driving conditions. IEEE Intelligent Vehicles Symposium, Proceedings içinde (ss. 101–106). doi:10.1109/IVS.2010.5548039
- Ghoddoosian, R., Galib, M. ve Athitsos, V. (2019). A realistic dataset and baseline temporal model for early drowsiness detection. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2019-June, 178–187. doi:10.1109/CVPRW.2019.00027
- Jo, J., Lee, S. J., Park, K. R., Kim, I. J. ve Kim, J. (2014). Detecting driver drowsiness using feature-level fusion and user-specific classification. Expert Systems with Applications, 41(4 PART 1), 1139–1152. doi:10.1016/j.eswa.2013.07.108
- King, D. E. (2009). Dlib-ml: A machine learning toolkit. Journal of Machine Learning Research, 10, 1755–1758.
- Li, X., Pfister, T., Huang, X., Zhao, G. ve Pietikainen, M. (2013). A Spontaneous Micro-expression Database: Inducement, collection and baseline. 2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition, FG 2013 içinde . doi:10.1109/FG.2013.6553717
- Massoz, Q., Langohr, T., Francois, C. ve Verly, J. G. (2016). The ULg multimodality drowsiness database (called DROZY) and examples of use. 2016 IEEE Winter Conference on Applications of Computer Vision, WACV 2016 içinde . doi:10.1109/WACV.2016.7477715
- McIntire, L. K., McKinley, R. A., Goodyear, C. ve McIntire, J. P. (2014). Detection of vigilance performance using eye blinks. Applied Ergonomics, 45(2 PB), 354–362. doi:10.1016/j.apergo.2013.04.020
- Reddy, B., Kim, Y. H., Yun, S., Seo, C. ve Jang, J. (2017). Real-Time Driver Drowsiness Detection for Embedded System Using Model Compression of Deep Neural Networks. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2017-July, 438–445. doi:10.1109/CVPRW.2017.59
- Sahayadhas, A., Sundaraj, K. ve Murugappan, M. (2012). Detecting driver drowsiness based on sensors: A review. Sensors (Switzerland), 12(12), 16937–16953. doi:10.3390/s121216937
- Soleimanloo, S. S., Wilkinson, V. E., Cori, J. M., Westlake, J., Stevens, B., Downey, L. A., … Howard, M. E. (2019). Eye-blink parameters detect on-road track-driving impairment following severe sleep deprivation. Journal of Clinical Sleep Medicine, 15(9), 1271–1284. doi:10.5664/jcsm.7918
- Suzuki, M., Yamamoto, N., Yamamoto, O., Nakano, T. ve Yamamoto, S. (2006). Measurement of driver’s consciousness by image processing - A method for presuming driver’s drowsiness by eye-blinks coping with individual differences. Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics içinde (C. 4, ss. 2891–2896). doi:10.1109/ICSMC.2006.385313
- Svensson, U. (2004). Blink Behaviour Based Drowsiness Detection: Method Development and Validation. (Applied Physics and Electrical Engineering, MSc). Linköping University
- Weng, C. H., Lai, Y. H. ve Lai, S. H. (2017). Driver drowsiness detection via a hierarchical temporal deep belief network. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) içinde (C. 10118 LNCS, ss. 117–133). Springer, Cham. doi:10.1007/978-3-319-54526-4_9
- Yan, W. J., Li, X., Wang, S. J., Zhao, G., Liu, Y. J., Chen, Y. H. ve Fu, X. (2014). CASME II: An improved spontaneous micro-expression database and the baseline evaluation. PLoS ONE, 9(1). doi:10.1371/journal.pone.0086041
- Yan, W. J., Wu, Q., Liu, Y. J., Wang, S. J. ve Fu, X. (2013). CASME database: A dataset of spontaneous micro-expressions collected from neutralized faces. 2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition, FG 2013 içinde . doi:10.1109/FG.2013.6553799
There are 20 citations in total.
Details
| Primary Language | Turkish |
|---|---|
| Subjects | Computer Software |
| Journal Section | Research Articles |
| Authors | |
| Early Pub Date | August 21, 2023 |
| Publication Date | August 21, 2023 |
| Acceptance Date | April 28, 2023 |
| Published in Issue | Year 2023 |