Driver drowsiness detection system using hybrid approach of convolutional neural network and bidirectional long short term memory (CNN_BILSTM)

S. P. Rajamohana; E.G. Radhika; S. Priya; S. Sangeetha

doi:10.1016/j.matpr.2020.11.898

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Driver drowsiness detection system using hybrid approach of convolutional neural network and bidirectional long short term memory (CNN_BILSTM)

S. P. Rajamohana E.G. Radhika S. Priya S. Sangeetha

Year: 2021 Journal: Materials Today Proceedings Vol: 45 Pages: 2897-2901 Publisher: Elsevier BV

DOI: 10.1016/j.matpr.2020.11.898

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Keywords:

Computer science Convolutional neural network Artificial intelligence Computer vision Face (sociological concept) Warning system Euclidean distance Term (time) Artificial neural network Pattern recognition (psychology) Real-time computing Telecommunications

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Topics

Sleep and Work-Related Fatigue

Social Sciences → Psychology → Experimental and Cognitive Psychology

Ergonomics and Musculoskeletal Disorders

Social Sciences → Psychology → Social Psychology

Fire Detection and Safety Systems

Physical Sciences → Engineering → Safety, Risk, Reliability and Quality

Driver drowsiness detection system using hybrid approach of convolutional neural network and bidirectional long short term memory (CNN_BILSTM)

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