Abstract
Nowadays, the use of public transportation is reducing and people prefer to use private transport because of its low cost, comfortable ride, and personal preferences. However, personal transport causes numerous real-world road accidents due to the conditions of the drivers' state such as drowsiness, stress, tiredness, and age during driving. In such cases, driver fatigue detection is mandatory to avoid road accidents and ensure a comfortable journey. To date, several complex systems have been proposed that have problems due to practicing hand feature engineering tools, causing lower performance and high computation. To tackle these issues, we propose an efficient deep learning-assisted intelligent fatigue and age detection system (FADS) to detect and identify different states of the driver. For this purpose, we investigated several neural computing-based methods and selected the most appropriate model considering its feasibility over edge devices for smart surveillance. Next, we developed a custom convolutional neural network-based system that is efficient for drowsiness detection where the drowsiness information is fused with age information to reach the desired output. The conducted experiments on the custom and publicly available datasets confirm the superiority of the proposed system over state-of-the-art techniques.