FADS: An Intelligent Fatigue and Age Detection System

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