A pattern recognition system for detecting use of mobile phones while driving

A pattern recognition system for detecting use of mobile phones while driving

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...

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Bibliographic Details
Published in:2014 International Conference on Computer Vision Theory and Applications (VISAPP) Vol. 2; pp. 411 - 418
Main Authors: Berri, Rafael A., Silva, Alexandre G., Parpinelli, Rafael S., Girardi, Elaine, Arthur, Rangel
Format: Conference Proceeding
Language:English
Published: SCITEPRESS 01-01-2014
Subjects:
Accuracy
Cell Phones
Cellular phones
Computer Vision
Driver Distraction
Genetic Algorithm
Kernel
Machine Learning
Mathematical model
Skin Segmentation
Support vector machines
Vehicles
Videos
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Summary: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.