A Pattern Recognition System for Detecting Use of Mobile Phones While Driving
This addresses driver distraction, a major cause of crashes, but is incremental as it applies existing SVM methods to a specific safety application.
The paper tackles the problem of detecting mobile phone use while driving by developing a pattern recognition algorithm, achieving a success rate of 91.57% on image classification and 87.43% on video-based detection over 3-second periods.
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.