Open AccessHybrid Deep Learning Based Visual System for In-Vehicle Safety
Author(s) -
Rajkumar Joghee Bhojan,
D. Ramyachitra,
Subramaniam Ganesan,
R. S. Rajkumar
Publication year - 2019
Publication title -
european journal of engineering and technology research
Language(s) - English
Resource type - Journals
ISSN - 2736-576X
DOI - 10.24018/ejeng.2019.4.4.1185
Subject(s) - computer science , artificial intelligence , pipeline (software) , deep learning , automotive industry , object detection , feature (linguistics) , object (grammar) , identification (biology) , face (sociological concept) , computer vision , machine learning , engineering , pattern recognition (psychology) , linguistics , botany , sociology , biology , programming language , aerospace engineering , social science , philosophy
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.