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

Author

Listed:
  • Rajkumar Joghee Bhojan
  • D. Ramyachitra
  • Subramanian Ganesan
  • Ragavi Rajkumar

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.

Suggested Citation

  • Rajkumar Joghee Bhojan & D. Ramyachitra & Subramanian Ganesan & Ragavi Rajkumar, 2019. "A Hybrid Deep Learning Based Visual System for In-Vehicle Safety," European Journal of Engineering and Technology Research, European Open Science, vol. 4(4), pages 43-47, April.
    • Handle: RePEc:epw:ejeng0:v:4:y:2019:i:4:id:61185
    DOI: 10.24018/ejeng.2019.4.4.1185
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    References listed on IDEAS

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    1. Volodymyr Mnih & Koray Kavukcuoglu & David Silver & Andrei A. Rusu & Joel Veness & Marc G. Bellemare & Alex Graves & Martin Riedmiller & Andreas K. Fidjeland & Georg Ostrovski & Stig Petersen & Charle, 2015. "Human-level control through deep reinforcement learning," Nature, Nature, vol. 518(7540), pages 529-533, February.
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