A Morphological Method for Driver Fatigue Monitoring

Авторы

  • Wen-Yen Wu

DOI:

https://doi.org/10.59573/emsj.8(5).2024.31

Ключевые слова:

driver fatigue, face detection, image processing, feature extraction, image recognition

Аннотация

The driver fatigue is one of the major problems for car accidents. In order to increase the safety, the automobile manufacturing companies are actively involved in the development of related technologies to detect driver fatigue. In this study, we will develop the computer vision technology to monitor driving behavior. The driver's head position will be detected from the video sequence at first. Second, face detection algorithm is applied to identify the driver’s face. The eyes and mouth will be detected in the following. The position of head, the blindness of eyes, and the openness of mouth will be analyzed to decide the driver state. The development of a visual-based detection will help to detect fatigue driving situations, and it will be very useful for the automobile industry.

Библиографические ссылки

Akhlaq, M., Sheltami, T. R., Helgeson, B., & Shakshuki, E. M. (2012). Designing an integrated driver assistance system using image sensors. Journal of Intelligent Manufacturing, 23, 2109-2132.

Al-Anizy, G. J., Nordin, M. J., & Razooq, M. M. (2015). Automatic driver drowsiness detection using haar algorithm and support vector machine techniques. Asia Journal of Applied Sciences, 8(2), 149-157.

Alioua, N., Amine, A., & Rziza, M. (2014). Driver’s fatigue detection based on yawning extraction. International Journal of Vehicular Technology, 2014(1), 678786.

Azim, T., Jaffar, M. A., & Mirza, A. M. (2014). Fully automated real time fatigue detection of drivers through fuzzy expert systems. Applied Soft Computing, 18, 25-38.

Bishop, R. (2000). Intelligent vehicle applications worldwide. IEEE Intelligent Systems and Their Applications, 15(1), 78-81.

Cai, H., Lin, Y., & Cheng, B. (2012). Coordinating multi‐level cognitive assistance and incorporating dynamic confidence information in driver‐vehicle interfaces. Human Factors and Ergonomics in Manufacturing & Service Industries, 22(5), 437-449.

Chakraborty, M., & Aoyon, A. N. H. (2014, April). Implementation of computer vision to detect driver fatigue or drowsiness to reduce the chances of vehicle accident. In 2014 International Conference on Electrical Engineering and Information & Communication Technology (pp. 1-5). IEEE.

Cheng, S. Y., & Trivedi, M. M. (2010). Vision-based infotainment user determination by hand recognition for driver assistance. IEEE Transactions on Intelligent Transportation Systems, 11(3), 759-764.

Cyganek, B., & Gruszczyński, S. (2014). Hybrid computer vision system for drivers' eye recognition and fatigue monitoring. Neurocomputing, 126, 78-94.

Dahiphale, W. E., Sathyanarayana, R., & Mukhedkar, M. M. (2015). Computer Vision System for Driver Fatigue Detection. International Journal of Advanced Research in Electronics and Communication Engineering, 4(9), 2331-2334.

Dasgupta, A., George, A., Happy, S. L., Routray, A., & Shanker, T. (2013). An on-board vision based system for drowsiness detection in automotive drivers. International Journal of Advances in Engineering Sciences and Applied Mathematics, 5, 94-103.

Fletcher, L., Apostoloff, N., Petersson, L., & Zelinsky, A. (2003). Vision in and out of vehicles. IEEE Intelligent Systems, 18(3), 12-17.

Hojjati-Emami, K., Dhillon, B., & Jenab, K. (2012). Reliability prediction for the vehicles equipped with advanced driver assistance systems (ADAS) and passive safety systems (PSS). International Journal of Industrial Engineering Computations, 3(5), 731-742.

Mattison, P. E. (1994). Practical Digital Video with Programming Examples in C. Wiley Press.

Narayanan, A., Kaimal, R. M., & Bijlani, K. (2014). Yaw estimation using cylindrical and ellipsoidal face models. IEEE Transactions on Intelligent Transportation Systems, 15(5), 2308-2320.

Trivedi, M. M., Gandhi, T., & McCall, J. (2007). Looking-in and looking-out of a vehicle: Computer-vision-based enhanced vehicle safety. IEEE Transactions on Intelligent Transportation Systems, 8(1), 108-120.

Опубликован

2024-12-09

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