A spatio-temporal CNN using BN-Inception features from sparse frames reaches 99.10% accuracy on 10-class driver distraction classification on the Distracted Driver Dataset while running in real time.
Real-time Distracted Driver Posture Classification
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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.
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cs.CV 1years
2019 1verdicts
UNVERDICTED 1representative citing papers
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Real-Time Driver State Monitoring Using a CNN Based Spatio-Temporal Approach
A spatio-temporal CNN using BN-Inception features from sparse frames reaches 99.10% accuracy on 10-class driver distraction classification on the Distracted Driver Dataset while running in real time.