A Pattern Recognition System for Detecting Use of Mobile Phones While Driving

TL;DR

This paper presents a vision-based system using SVM to detect mobile phone use while driving, achieving over 91% accuracy in images and 87% in real-time video segments, aiming to improve road safety.

Contribution

It introduces a novel algorithm for identifying phone use during driving using image features and SVM classification, with promising real-world applicability.

Findings

Achieved 91.57% success rate in image classification.

Correctly classified 87.43% of 3-second video segments.

Support Vector Machine with Polynomial kernel was most effective.

Abstract

It is estimated that 80% of crashes and 65% of near collisions involved drivers inattentive to traffic for three seconds before the event. This paper develops an algorithm for extracting characteristics allowing the cell phones identification used during driving a vehicle. Experiments were performed on sets of images with 100 positive images (with phone) and the other 100 negative images (no phone), containing frontal images of the driver. Support Vector Machine (SVM) with Polynomial kernel is the most advantageous classification system to the features provided by the algorithm, obtaining a success rate of 91.57% for the vision system. Tests done on videos show that it is possible to use the image datasets for training classifiers in real situations. Periods of 3 seconds were correctly classified at 87.43% of cases.