  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.
