Title: Real-time Distracted Driver Posture Classification,
Abstract: In this paper, we present a new dataset for "distracted driver" posture
estimation. In addition, we propose a novel system that achieves 95.98% driving
posture estimation classification accuracy. The system consists of a
genetically-weighted ensemble of Convolutional Neural Networks (CNNs). We show
that a weighted ensemble of classifiers using a genetic algorithm yields in
better classification confidence. We also study the effect of different visual
elements (i.e. hands and face) in distraction detection and classification by
means of face and hand localizations. Finally, we present a thinned version of
our ensemble that could achieve a 94.29% classification accuracy and operate in
a realtime environment.