Abstract
A driver’s lack of concentration or distraction is one of the main reasons for causing road accidents. Thus, increasing the driver’s awareness at the ideal moment will reduce the possibility of an accident of any kind. There were around 155 thousand accidents in India, and around 40 percent of accidents were caused by driver distraction, mainly due to driver drowsiness. Detecting drowsiness or fatigue prior to an accident will help reduce these accidents. There are several ways we may execute this. One of the easiest and most effective ways is through artificial intelligence and machine learning algorithms. We consider both physiological and behavioral categories, such as face movement and eye closure movements, to detect drowsiness. Further, training a particular model with different types of eye movements helps in detecting driver conditions. Driver drowsiness detection can be improved by continuously monitoring the driver via video, which helps in real-world applications, and by expanding the dataset through training, we get high accuracy and unrecognizable losses. Therefore, in this paper, we use the MRL dataset, which contains images from every angle and in every shade. To train the existing model with this dataset, we use image processing techniques and classification techniques to distinguish images of open and closed eyes on the basis of accuracy and loss function, a comparison of SVM (Support Vector Machine) and CNN (Convolutional Neural Network) models has been performed. As a result, CNN is considerably better than SVM and it is an effective technique for dozy detection.
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References
Albasrawi, R., Fadhil, F.F., Ghazal, M.T.: Driver drowsiness monitoring system based on facial landmark detection with convolutional neural network for prediction. Bull. Electr. Eng. Inform. 11(5), 2637–2644 (2022)
Bajaj, J.S., Kumar, N., Kaushal, R.K.: Comparative study to detect driver drowsiness. In: 2021 International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE), pp. 678–683 (2021)
Caryn, F.H., Rahadianti, L.: Driver drowsiness detection based on drivers’ physical behaviours: a systematic literature review. Comput. Eng. Appl. J. 10(3), 161–175 (2021)
Chaabene, S., Bouaziz, B., Boudaya, A., Hökelmann, A., Ammar, A., Chaari, L.: Convolutional neural network for drowsiness detection using EEG signals. Sensors 21(5), 1734 (2021)
Deng, W., Ruoxue, W.: Real-time driver-drowsiness detection system using facial features. IEEE Access 7, 118727–118738 (2019)
Ferreira, P.M., et al.: AUTOMOTIVE: a case study on automatic multimodal drowsiness detection for smart vehicles (2021)
Roopalakshmi, R., Rathod, J.A., Shetty, A.S., Supriya, K.: Driver drowsiness detection system based on visual features. In: 2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT), pp. 1344–1347 (2018)
Horberry, T., Anderson, J., Regan, M.A., Triggs, T.J., Brown, J.: Driver distraction: the effects of concurrent in-vehicle tasks, road environment complexity and age on driving performance. Accid. Anal. Prev. 38(1), 185–191 (2006)
Kiashari, S.E.H., Nahvi, A., Bakhoda, H., Homayounfard, A., Tashakori, M.: Evaluation of driver drowsiness using respiration analysis by thermal imaging on a driving simulator. Multimed. Tools Appl. 79(25–26), 17793–17815 (2020). https://doi.org/10.1007/s11042-020-08696-x
Kumar, A., Patra, R.: Driver drowsiness monitoring system using visual behaviour and machine learning. In: 2018 IEEE Symposium on Computer Applications Industrial Electronics (ISCAIE), pp. 339–344 (2018)
LaRocco, J., Le, M.D., Paeng, D.G.: A systemic review of available low-cost EEG headsets used for drowsiness detection. Front. Neuroinform. 14, 42 (2020)
Moujahid, A., Dornaika, F., Arganda-Carreras, I., Reta, J.: Efficient and compact face descriptor for driver drowsiness detection. Expert Syst. Appl. 168, 114334 (2021)
Niloy, A.R., Chowdhury, A.I., Sharmin, N., et al.: A brief review on different driver’s drowsiness detection techniques. Int. J. Image Graph. Sign. Proc. 10(3), 41 (2020)
Stancin, I., Cifrek, M., Jovic, A.: A review of EEG signal features and their application in driver drowsiness detection systems. Sensors 21(11), 3786 (2021)
Vesselenyi, T., Moca, S., Rus, A., Mitran, T., Tătaru, B.: Driver drowsiness detection using ANN image processing. In: IOP Conference Series: Materials Science and Engineering, vol. 252(1), p. 012097 (2017)
Victoria, D.R.S., Mary, D.G.R.: Driver drowsiness monitoring using convolutional neural networks. In: 2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS), pp. 1055–1059 (2021)
You, F., Gong, Y., Tu, H., Liang, J., Wang, H.A.: fatigue driving detection algorithm based on facial motion information entropy. J. Adv. Transp. 2020, 1–17 (2020)
Zandi, A.S., Quddus, A., Prest, L., Comeau, F.J.: Non-intrusive detection of drowsy driving based on eye tracking data. Transp. Res. Rec. 2673(6), 247–257 (2019)
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Kamboj, M., Bhagya Sri, J., Banik, T., Ojha, S., Kadian, K., Dwivedi, V. (2023). Driver Dozy Discernment Using Neural Networks with SVM Variants. In: Singh, M., Tyagi, V., Gupta, P., Flusser, J., Ören, T. (eds) Advances in Computing and Data Sciences. ICACDS 2023. Communications in Computer and Information Science, vol 1848. Springer, Cham. https://doi.org/10.1007/978-3-031-37940-6_40
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DOI: https://doi.org/10.1007/978-3-031-37940-6_40
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