A driver fatigue recognition model based on information fusion and dynamic Bayesian network
By Guosheng Yang, Yingzi Lin, Prabir Bhattacharya. Information Sciences, 2010
328 citations recorded by the source corpus.
Subfields: Biomedical Engineering, Cardiology and Cardiovascular Medicine, Experimental and Cognitive Psychology
View Paper: https://www.semanticscholar.org/paper/059e25f5c5e0312743917dd415ed900276fb1624
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In Biomedical Engineering
In Cardiology and Cardiovascular Medicine
In Experimental and Cognitive Psychology
Citations in the corpus
- A generative restricted Boltzmann machine based method for high-dimensional motion data modeling (2015, methodology): “Images are from CK+ data s Model-based methods include probabilistic graphical models such as hidden Markov models [5], dynamic Bayesian networks [16], conditional random fields [17], and their variants.”
- Driver Fatigue Detection Systems: A Review (2019, methodology): “[66] present an approach based on both visual and physiological features using DBN.”
- Driver Inattention Monitoring System for Intelligent Vehicles: A Review (2009, unknown): “Yang et al. [66] employed a dynamic BN with EEG and ECG to estimate fatigue.”