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Distracted driver classification using deep learning

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Abstract

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.

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Notes

  1. An insurance company; its headquarters are located in Bloomington, IL, USA

  2. A platform for data science and predictive models competitions

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Authors and Affiliations

  1. College of Computing and Information Technology, Shagra University, Shagra, Saudi Arabia

    Munif Alotaibi

  2. College of Computer Science and Information Technology, University of Tabuk, Tabuk, Saudi Arabia

    Bandar Alotaibi

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  1. Munif Alotaibi
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  2. Bandar Alotaibi
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Correspondence to Munif Alotaibi.

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Alotaibi, M., Alotaibi, B. Distracted driver classification using deep learning. SIViP 14, 617–624 (2020). https://doi.org/10.1007/s11760-019-01589-z

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  • DOI: https://doi.org/10.1007/s11760-019-01589-z

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