Details

Autor(en) / Beteiligte

• Alotaibi, Munif
• Alotaibi, Bandar

Titel

Distracted driver classification using deep learning

Ist Teil von

• Signal, image and video processing, 2020-04, Vol.14 (3), p.617-624

Ort / Verlag

London: Springer London

Erscheinungsjahr

2020

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• One of the most challenging topics in the field of intelligent transportation systems is the automatic interpretation of the driver’s behavior. This research investigates distracted driver posture recognition as a part of the human action recognition framework. Numerous car accidents have been reported that were caused by distracted drivers. Our aim was to improve the performance of detecting drivers’ distracted actions. The developed system involves a dashboard camera capable of detecting distracted drivers through 2D camera images. We use a combination of three of the most advanced techniques in deep learning, namely the inception module with a residual block and a hierarchical recurrent neural network to enhance the performance of detecting the distracted behaviors of drivers. The proposed method yields very good results. The distracted driver behaviors include texting, talking on the phone, operating the radio, drinking, reaching behind, fixing hair and makeup, and talking to the passenger.