Real-time Distracted Driver Posture Classification

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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.