Smart Detection System for Driver Distraction: Enhanced Support Vector Machine classifier using Analytical Hierarchy Process technique

Abstract

Driving distraction is the most significant source of motor-vehicle accidents. In 2018 in United States there were 2,800 mortality cases and over 400,000 were injured in distracted driver collision according to Center for Disease Control. Various research techniques have been proposed in literature based on machine learning to complex deep learning to learn the driving pattern and identify the distraction cause to driver. As the model are employed in a real time environment a delay in smallest internal of time can lead to a major accident. The objective of the research is to decrease the computational time of this model without compromising the accuracy, to achieve this, we employed a data-centric approach with enhancement of Support Vector Machine, resulting in significant improvements. In our attempt to increase the accuracy and reduce the training time, we experiment on various feature extraction and dimensionality reduction techniques on Improved SVM. While testing the average time taken by one image to check and respond with the classification whether the driver is distracted or not, our model was able to detect one image in 0.53 seconds which is significantly low.