A Hybrid Deep Learning Based Visual System for In-Vehicle Safety

A Hybrid Deep Learning Based Visual System for In-Vehicle Safety

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Submitted Mar 2, 2019
Published Apr 9, 2019
DOI https://doi.org/10.24018/ejeng.2019.4.4.1185
Abstract viewed = 549 times
Issue
Vol. 4 No. 4: APRIL 2019
Section
Articles

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  •   Rajkumar Joghee Bhojan

    •    Wipro Technologies
  •   D. Ramyachitra

    •    Bharathiar University, Coimbatore, India
  •   Subramanian Ganesan

    •    Department of Electrical and Computer Engineering, Oakland University, Rochester, USA
  •   Ragavi Rajkumar

    •    Sharon High School, MA, USA.

Abstract

In the automotive industry, researchers, AI experts, and developers are actively pushing deep learning based approaches for In-vehicle safety.    In this research paper, we propose a hybrid deep learning based visual system for providing feedback to the driver in a non-intrusive manner. We describe a hybrid SSD-RBM (Single Shot MultiBox Detector - Restricted Boltzmann Machine)  model for face feature identification. In this system, object detection, object tracking, and observations are processed through a full pipeline of image processing and detect the driver's movements and generate a safe and efficient action plan in real time. This in-vehicle interactive system assists drivers in regulating driving performance and avoiding hazards.


Keywords: Computer Vision, Deep Learning, Driver Alert, In-vehicle Safety.

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How to Cite
[1]
Joghee Bhojan, R., Ramyachitra, D., Ganesan, S. and Rajkumar, R. 2019. A Hybrid Deep Learning Based Visual System for In-Vehicle Safety. European Journal of Engineering and Technology Research. 4, 4 (Apr. 2019), 43–47. DOI:https://doi.org/10.24018/ejeng.2019.4.4.1185.