Movatterモバイル変換

[0]ホーム
URL:

Skip to main content

Advertisement

Springer Nature Link
Log in

Behavior-based driver fatigue detection system with deep belief network

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Traffic accidents as a result of driver fatigue and drowsiness have caused many injuries and deaths. Therefore, driver fatigue detection and prediction system have been recognized as important potential research areas to prevent accidents caused by fatigue and drowsiness while driving. In this study, driver fatigue is determined by using behavior-based measurement information. Recent studies show that deep neural network is trending state-of-the-art machine learning approaches. Hence, we propose the deep belief network (DBN) model, a deep learning type, used for classification of the symptoms of fatigue in this study. DBN structure is a kind of neural network. The number of hidden layers within the network and the number of units in each hidden layer play important roles in the design of any neural network. Therefore, the hidden layer and the count of units in the DBN model designed in this paper have been selected as a result of various experiments. A greedy method has been adopted to adjust the structure of the deep belief network. Subsequently, the proposed DBN architecture test on KOU-DFD, YawDD and Nthu-DDD datasets. Comparative and experimental results concluded that the proposed DBN architecture is as robust as the other approaches found in the literature and achieves an accuracy rate of approximately 86%.

This is a preview of subscription content,log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Japan)

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Abtahi S, Omidyeganeh M, Shirmohammadi S, Hariri B. YawDD: A yawning detection dataset. In Proceedings of the 5th ACM Multimedia Systems Conference 2014 (pp. 24–28).https://doi.org/10.1145/2557642.2563678

  2. Aditya S Restricted Boltzmann Machines–Simplified,https://towardsdatascience.com/restricted-boltzmann-machines-simplified-eab1e5878976. Accessed 10 Feb 2020

  3. Bouwmans T, Javed S, Sultana M, Jung SK (2019) Deep neural network concepts for background subtraction: a systematic review and comparative evaluation. Neural Netw 117:8–66.https://doi.org/10.1016/j.neunet.2019.04.024

    Article  Google Scholar 

  4. Chen S, Wang Z, Chen W (2021) Driver drowsiness estimation based on factorized bilinear feature fusion and a long-short-term recurrent convolutional network. Information 12(1):3.https://doi.org/10.3390/info12010003

    Article  Google Scholar 

  5. Dua M, Singla R, Raj S, Jangra A (2021) Deep CNN models-based ensemble approach to driver drowsiness detection. Neural Comput Appl 33(8):3155–3168 https://doi.org/10.1007/s00521-020-05209-7

    Article  Google Scholar 

  6. Dwivedi K, Biswaranjan K, Sethi A (2014) Drowsy driver detection using representation learning. In 2014 IEEE international advance computing conference (IACC) (pp. 995–999). IEEE.https://doi.org/10.1109/IAdCC.2014.6779459

  7. Fan X, Yin B, Sun Y (2008) Nonintrusive driver fatigue detection. In 2008 IEEE International Conference on Networking, Sensing and Control (pp. 905–910). IEEE.https://doi.org/10.1109/ICNSC.2008.4525345

  8. Hajinoroozi M, Jung TP, Lin CT, Huang Y (2015) Feature extraction with deep belief networks for driver's cognitive states prediction from EEG data. In 2015 IEEE China Summit and International Conference on Signal and Information Processing (ChinaSIP) (pp. 812–815). IEEE.https://doi.org/10.1109/ChinaSIP.2015.7230517

  9. Hanafi MFFM, Nasir MSFM, Wani S, Abdulghafor RAA, Gulzar Y, Hamid Y (2021) A real time deep learning based driver monitoring system. Int J Percept Cogn Comput 7(1):79–84

    Google Scholar 

  10. Hinton GE, Salakhutdinov RR (2006) Reducing the dimensionality of data with neural networks. Science 313(5786):504–7.https://doi.org/10.1126/science.1127647

    Article MathSciNet MATH  Google Scholar 

  11. Hinton GE, Osindero S, Teh YW (2006) A fast learning algorithm for deep belief nets. Neural Comput 18(7):1527–1554.https://doi.org/10.1162/neco.2006.18.7.1527

    Article MathSciNet MATH  Google Scholar 

  12. Hong T, Qin H (2007) Drivers drowsiness detection in embedded system. In 2007 IEEE International Conference on Vehicular Electronics and Safety (pp. 1–5). IEEE.https://doi.org/10.1109/ICVES.2007.4456381.

  13. Jabbar R, Al-Khalifa K, Kharbeche M, Alhajyaseen W, Jafari M, Jiang S (2018) Real-time driver drowsiness detection for android application using deep neural networks techniques. Procedia computer science 1(130):400–407.https://doi.org/10.1016/j.procs.2018.04.060

    Article  Google Scholar 

  14. Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. Adv Neural Inf Process Syst 25:1097–1105

    Google Scholar 

  15. Koenig A, Rehder T, Hohmann S (2017) Exact inference and learning in hybrid Bayesian Networks for lane change intention classification. In 2017 IEEE Intelligent Vehicles Symposium (IV) (pp. 1535–1540). IEEE.https://doi.org/10.1109/IVS.2017.7995927

  16. Lalonde M, Byrns D, Gagnon L, Teasdale N, Laurendeau D (2007) Real-time eye blink detection with GPU-based SIFT tracking. In Fourth Canadian Conference on Computer and Robot Vision (CRV’07) (pp. 481–487). IEEE.https://doi.org/10.1109/CRV.2007.54.

  17. Latha CP, Priya M (2016) A review on deep learning algorithms for speech and facial emotion recognition. APTIKOM J Comp Sci Inform Technol 1(3):92–108

    Article  Google Scholar 

  18. Le Roux N, Bengio Y (2008) Representational power of restricted Boltzmann machines and deep belief networks. Neural Comput 20(6):1631–1649.https://doi.org/10.1162/neco.2008.04-07-510

    Article MathSciNet MATH  Google Scholar 

  19. LeCun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278–2324.https://doi.org/10.1109/5.726791

    Article  Google Scholar 

  20. Li P, Jiang W, Su F (2016) Single-channel EEG-based mental fatigue detection based on deep belief network. In 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 367–370). IEEE.https://doi.org/10.1109/EMBC.2016.7590716

  21. Lyu J, Zhang H, Yuan Z (2016) Joint shape and local appearance features for real-time driver drowsiness detection. In Asian Conference on Computer Vision (pp. 178–194). Springer, Cham.https://doi.org/10.1007/978-3-319-54526-4_14

  22. Ma J, Zhang J, Gong Z, Du Y (2018) Study on fatigue driving detection model based on steering operation features and eye movement features. In 2018 IEEE 4th International Conference on Control Science and Systems Engineering (ICCSSE) (pp. 472–475). IEEE.https://doi.org/10.1109/CCSSE.2018.8724836

  23. Mohamed AR, Dahl GE, Hinton G (2011) Acoustic modeling using deep belief networks. IEEE Trans Audio Speech Lang Process 20(1):14–22.https://doi.org/10.1109/TASL.2011.2109382

    Article  Google Scholar 

  24. Mohamed AR, Yu D, Deng L (2010) Investigation of full-sequence training of deep belief networks for speech recognition. In eleventh annual conference of the international speech communication association

  25. Ouyang T, He Y, Li H, Sun Z, Baek S (2019) Modeling and forecasting short-term power load with copula model and deep belief network. IEEE Trans Emerg Topics Comput Intellig 3(2):127–136.https://doi.org/10.1109/TETCI.2018.2880511

    Article  Google Scholar 

  26. Salakhutdinov R, Mnih A, Hinton G (2007) Restricted Boltzmann machines for collaborative filtering. In Proceedings of the 24th international conference on Machine learning (pp. 791–798).https://doi.org/10.1145/1273496.1273596

  27. Stephanidis C (2016) editor. HCI International 2016–Poster’s Extended Abstracts: 18th International Conference, HCI International 2016, Toronto, Canada, July 17–22, Proceedings. Springer; Jul 4.https://doi.org/10.1007/978-3-319-40548-3

  28. Savaş BK, Becerikli Y (2018) Real time driver fatigue detection based on SVM algorithm. In 2018 6th International Conference on Control Engineering & Information Technology (CEIT) (pp. 1–4). IEEE.https://doi.org/10.1109/CEIT.2018.8751886

  29. Savaş BK, Becerikli Y (2017) Development of Driver Fatigue Detection System By Using Video Images Innovations in Intelligent Systems and Applications Conference (ASYU) Oct 5.

  30. Savaş BK, Becerikli Y (2020) Real time driver fatigue detection system based on multi-task ConNN. IEEE Access 3(8):12491–12498.https://doi.org/10.1109/ACCESS.2020.2963960

    Article  Google Scholar 

  31. Savaş, BK, Becerikli Y (2021) A Deep Learning Approach to Driver Fatigue Detection via Mouth State Analyses and Yawning Detection

  32. Weng CH, Lai YH, Lai SH (2016) Driver drowsiness detection via a hierarchical temporal deep belief network. In Asian Conference on Computer Vision (pp. 117–133). Springer, Cham.https://doi.org/10.1007/978-3-319-54526-4_9.

  33. Yan C, Coenen F, Zhang B (2016) Driving posture recognition by convolutional neural networks. IET Comput Vision 10(2):103–114.https://doi.org/10.1049/iet-cvi.2015.0175

    Article  Google Scholar 

  34. Zhang W, Murphey YL, Wang T, Xu Q (2015) Driver yawning detection based on deep convolutional neural learning and robust nose tracking. In 2015 International Joint Conference on Neural Networks (IJCNN) (pp. 1–8). IEEE.https://doi.org/10.1109/IJCNN.2015.7280566

  35. Zhao L, Wang Z, Wang X, Liu Q (2017) Driver drowsiness detection using facial dynamic fusion information and a DBN. IET Intel Transport Syst 12(2):127–133.https://doi.org/10.1049/iet-its.2017.0183

    Article  Google Scholar 

  36. Zhao Z, Guo J, Ding E, Zhu Z, Zhao D (2015) Terminal replacement prediction based on deep belief networks. In 2015 International Conference on Network and Information Systems for Computers (pp. 255–258). IEEE.https://doi.org/10.1109/ICNISC.2015.96

  37. Zheng Z, Dai S, Liang Y, Xie X (2019) Driver fatigue analysis based on upper body posture and DBN-BPNN model. In 2019 IEEE 4th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC) (Vol. 1, pp. 574–581). IEEE.https://doi.org/10.1109/IAEAC47372.2019.8997925

Download references

Acknowledgements

This work was supported by Kocaeli University, Scientific Research Project Department (A-2-2 Doctoral Thesis Support Projects, Project no:2017/087).

Author information

Authors and Affiliations

  1. Computer Engineering Department, Kocaeli University, Kocaeli, Turkey

    Burcu Kır Savaş & Yaşar Becerikli

Authors
  1. Burcu Kır Savaş

    You can also search for this author inPubMed Google Scholar

  2. Yaşar Becerikli

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence toBurcu Kır Savaş.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kır Savaş, B., Becerikli, Y. Behavior-based driver fatigue detection system with deep belief network.Neural Comput & Applic34, 14053–14065 (2022). https://doi.org/10.1007/s00521-022-07141-4

Download citation

Keywords

Access this article

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Japan)

Instant access to the full article PDF.

Advertisement

[8]ページ先頭
©2009-2025 Movatter.jp