## 5 Conclusions and Future Work  
Distraction detection is an important feature in modern semi-assisted vehicles, but it is difficult to detect it accurately due to the large number of factors involved. Numerous research works attempted to detect the distraction of a driver while driving, but most of them failed to achieve all the objectives of accuracy, simplicity, cost-effectiveness, and timeliness. This paper proposes a feature-based approach that outperforms state-of-the-art methods, including EM-Stat, TA, AM-Mouth, Ali, Lee, and Frid on the DrivFace, Boston University, FT-UMT, and Pointing'04 datasets. The proposed approach also yields better percentage accuracy than the deep learning models, namely: ResNet-50, ResNet-101, VGG-19, Inception-V3, MobileNet, and Xception. The proposed approach is compared with its variants and gives better results. The deep learning models are end to end and provide better accuracy compared to the approach of hand-crafted features. However, deep learning approaches also come with a cost and require high processing power, large datasets for training, and long training time. On the contrary, the classical approach works better than deep networks on smaller datasets. Our proposed approach requires computationally cheaper hardware and fewer data. Our technique is simple, accurate, and fast enough to be implemented in the real world to detect a driver's distraction from head panning. As an extension of this work, the factors such as eye gaze movement, driver behavior while driving, driver's facial expressions, driver actions, and vehicle movement can be considered. Distraction detection of a driver in night driving or dim light also needs to be investigated. A distraction detection system should be invariant to various factors, including camera distortion, projective geometry, multi-source non-Lambertian lighting, as well as the movement of facial muscles, biological appearance, facial expression, use of cell phone, hats, and glasses. Conceptualization, S.F.A. and A.S.A. ; methodology, S.F.A. ; software, S.F.A. ; validation, S.F.A. ; writing--original draft preparation, S.F.A. and A.S.A. ; writing--review and editing, S.F.A., A.S.A., M.J.A., A.Y. and R.D. ; supervision, M.J.A. ; project administration, A.Y. and R.D. ; funding acquisition, R.D. All authors have read and agreed to the published version of the manuscript. This work was supported by the National ICT R&D under grant no. NICTDF/NGIRI/2013-14/Crops/2 and the University of Management & Technology, Lahore, Pakistan. Not applicable. D.A. * [PERSON] and [PERSON] (2019) [PERSON]; [PERSON] [PERSON] A Comprehensive Survey of Driving Monitoring and Assistance Systems. _Sensors_**2019**, _19_, 2574. [CrossRef] [PubMed]
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