Abstract
The accident rate in Saudi Arabia is quite high due to several factors such as speeding, lousy weather, pedestrian unawareness, etc. For the past year, Kingdom of Saudi Arabia suffered from a significant loss of human life and economical due to a traffic accident. Ministry of Interior Riyadh Traffic department reporting that traffic accident has made financial loss about 21 billion riyals a year. The traffic accident has increased rapidly, six injuries per eight incidents are caused by a traffic accident, whereas the global ratio is one injury for every eight accidents. This paper proposed unique solution by using two primary cameras for tracking the driver and pedestrian. The recognition rate of facial tracking have been evaluated through several machine learning algorithm and Tree has achieved the highest recognition rate with 87% followed by KNN with 77%. The result is quite promising with this double tracking: facial tracking to track the drowsiness and awareness while pedestrian tracking brake analysis for the object in front of or behind the vehicle. The future work with advanced sensor such as sonar and high speed camera might improve the accuracy and efficiency of the system.
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Acknowledgement
This work was supported by the Deanship of Scientific Research (DSR), King Abdulaziz University, Jeddah Saudi Arabia. The authors, therefore, gratefully acknowledge the DSR technical and financial support.
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Basori, A.H., Malebary, S.J. (2021). iDriveAR: In-Vehicle Driver Awareness and Drowsiness Framework Based on Facial Tracking and Augmented Reality. In: Gupta, N., Prakash, A., Tripathi, R. (eds) Internet of Vehicles and its Applications in Autonomous Driving. Unmanned System Technologies. Springer, Cham. https://doi.org/10.1007/978-3-030-46335-9_7
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