  According to the World Health Organization, distracted driving is one of the
leading cause of motor accidents and deaths in the world. In our study, we
tackle the problem of distracted driving by aiming to build a robust
multi-class classifier to detect and identify different forms of driver
inattention using the State Farm Distracted Driving Dataset. We utilize
combinations of pretrained image classification models, classical data
augmentation, OpenCV based image preprocessing and skin segmentation
augmentation approaches. Our best performing model combines several
augmentation techniques, including skin segmentation, facial blurring, and
classical augmentation techniques. This model achieves an approximately 15%
increase in F1 score over the baseline, thus showing the promise in these
techniques in enhancing the power of neural networks for the task of distracted
driver detection.
