Real-time Distracted Driver Posture Classification

Yehya Abouelnaga, Hesham M. Eraqi, Mohamed N. Moustafa

Oct 12, 2018 NIPS 2018 Workshop MLITS Submission readers: everyone
  • 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.
  • TL;DR: Distracted driver posture classification using ConvNets and genetic algorithms
  • Keywords: distracted driver, genetic algorithm, convolutional neural networks
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