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

Rajkumar Joghee Bhojan, D. Ramyachitra, Subramanian Ganesan and Ragavi Rajkumar
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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.

European Journal of Engineering and Technology Research, 2019, vol. 4, issue 4, 43-47

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. (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:epw:ejeng0:v:4:y:2019:i:4:id:61185

DOI: 10.24018/ejeng.2019.4.4.1185

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