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


  •   Rajkumar Joghee Bhojan

  •   D. Ramyachitra

  •   Subramanian Ganesan

  •   Ragavi Rajkumar


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
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 Research and Science. 4, 4 (Apr. 2019), 43-47. DOI:https://doi.org/10.24018/ejers.2019.4.4.1185.