Smart Driving Application for a Safe Ride- A Comparative Analysis of Drowsiness Detection Methods
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Abstract
Covid pandemic has given a boost to online shopping and hence the product delivery. As a result, the number of services like carpooling, product delivery for online shopping, food delivery services like zomato, swiggy, ubereats, etc. have increased tremendously. These services have added number of drivers to the already existing large number of automobile users on road. Also these services are provided without any restricted time duration causing considerable increase in road traffic during rush hours. These conditions result into driver fatigue, rash driving, micro sleeps and even drowsiness. Driving in these peak times is not only dangerous but also may result into accidents and casualties. Fatigued driving is consequently converted into drowsiness and is the major cause of road accidents. Worldwide many people become victim of it and lose their lives due to drowsiness. However, if the driver fatigue is detected early and some prior indication of it is given, it may prevent many accidents and can save lives. Many automobile vendors are providing driver assistance solutions inbuilt in the cars. However, the accuracy of these solutions is again a point of discussion. To address this issue, the presented paper provides a detailed survey on different non- invasive methods of real-time detection of driver fatigue and drowsiness with their comparative solutions.
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References
- Biswal, A. K., Singh, D., Pattanayak, B. K., Samanta, D., and Yang, M.-H. 2021. Iot-based smart alert system for drowsy driver detection. Wireless Communications and Mobile Computing 2021.
- Boyko, N., Basystiuk, O., and Shakhovska, N. 2018. Performance evaluation and comparison of software for face recognition, based on dlib and opencv library. In 2018 IEEE Second International Conference on Data Stream Mining & Processing (DSMP). IEEE, 478–482.
- Celecia, A., Figueiredo, K., Vellasco, M., and Gonzalez, R. ´ 2020. A portable fuzzy driver drowsiness estimation system. Sensors 20, 15, 4093.
- Deng, J. 2009. W., dong, r. socher, l. In J., Li,, K., Li,, and, L., Fei, Fei., Imagenet:, A large-scale, hierarchical, image, database., In, IEEE, Conference, on Computer, Vision, and Pattern, Recogni, tion,(CVPR),, pages. 248–255.
- Dlib. 2018. Dlib c++ toolkit. http://dlib.net/.
- Jabbar, R., Al-Khalifa, K., Kharbeche, M., Alhajyaseen, W., Jafari, M., and Jiang, S. 2018. Real-time driver drowsiness detection for android application using deep neural networks techniques. Procedia computer science 130, 400–407.
- Mardi, Z., Ashtiani, S. N. M., and Mikaili, M. 2011. Eeg-based drowsiness detection for safe driving using chaotic features and statistical tests. Journal of medical signals and sensors 1, 2, 130.
- Rosebrock, A. 2017. Drowsiness detection with opencv. Pyimagesearch.
- Sagonas, C., Tzimiropoulos, G., Zafeiriou, S., and Pantic, M. 2013. 300 faces in-thewild challenge: The first facial landmark localization challenge. In Proceedings of the IEEE International Conference on Computer Vision Workshops. 397–403.
- Viola, P. and Jones, M. 2001. Rapid object detection using a boosted cascade of simple features. In Proceedings of the 2001 IEEE computer society conference on computer vision and pattern recognition. CVPR 2001. Vol. 1. Ieee, I–I.
- Weng, C.-H., Lai, Y.-H., and Lai, S.-H. 2016. Driver drowsiness detection via a hierarchical temporal deep belief network. In Asian Conference on Computer Vision. Springer, 117–133.