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

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  •   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.


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

References

. Global Status Report on Road Safety 2018.

https://www.nhtsa.gov/research-data

. Steven Landry, Myounghoon, Pavlo, Jan Hammerschmidt, "Report on the in-vehicle auditory interactions workshop_ taxonomy, challenges, and approaches", AutomotiveUI, 2015.

. V. Mnih et al., “Human-level control through deep reinforcement learning,” Nature, vol. 518, pp. 529–533, 2015.

. Y. Bengio, “Learning deep architectures for AI,” Found. Trends Mach. Learn., vol. 2, no. 1, pp. 1–127, 2009.

I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning.Cambridge, MA, USA: MIT Press, 2016.

. Sarkar, Dipanjan, “Practical Machine Learning with Python - A Problem-Solver's Guide to Building Real-World Intelligent Systems”, 2016, Apress publications.

. HaiguangWen Kuan Han, Junxing Shi, Yizhen Zhang, Eugenio Culurciello, Zhongming Liu, "Deep Predictive Coding Network for Object Recognition", International Conference on Machine Learning (ICML), 2018.

. Reinhard Klette, "Computer Vision in Vehicles - A Brief Introduction", Technical Report, February 2014, ResearchGate.

. https://opencv.org/about.html

. Rajkumar Joghee Bhojan, Ramyachitra, Vivekanandan, Subramaniam Ganesan, “A Machine Learning Based Approach for Detecting Non-Deterministic Tests and its Analysis in Mobile Application Testing”, International Journal of Advanced Research in Computer Science, ISSN No. 0976-5697, pp. 220-223.

. Geoffrey Hinton, "A Practical Guide to Training Restricted Boltzmann Machines", 2010.

. D. H. Hubel and T. N. Wiesel, “Receptive fields, binocular interaction, and functional architecture in the cat’s visual cortex,” The Journal of Physiology, vol. 160, pp. 106–154, 1962.

. Athanasios Voulodimos, Nikolaos Doulamis, Anastasios Doulamis and Effychios Protopapadakis, “Deep Learning for Computer Vision: A Brief Review”, Computational Intelligence and Neuroscience, Vol 2018, Hindawi.

. Ali Mollahosseini et al. AffectNet: A Database for facial expression, Valence, and Arousal Computing in the Wild Department of Electrical and Computer Engineering, University of Denver, Denver, CO, 80210, 2015.

. Mirafe R Prospero, Edson B Lagmayo, Anndee Christian L Tumulak, "Skybiometry and AffectNet on Facial Emotion Recognition Using Supervised Machine Learning Algorithms", ICCCV 2018, Singapore.

. David Acuna, Huan Ling, Amlan Kar, Sanja Fidler, "Efficient Interactive Annotation of Segmentation Datasets with Polygon-RNN++", CVPR, 2018.

. Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Chen-Yang Fu, Alexander C. Berg, "SSD: Single Shot MultiBox Detector", arXiv, 2016.

. Ronghang Hu, Piotr Dollar, Kaiming He, Trevor Darrel, Ross Girshick, "Learning to Segment Every Thing", Computer Vision and Pattern Recognition, 2018.

. Shaoshan Liu, Jie Tang, Zhe Zhang, Jean-Luc, "Computer Architectures for Autonomous Driving", Computer, Volume: 50, Issue: 8, 2017, IEEE Computer Society, DOI:10.1109/MC.2017.3001256.

. Chieh-Chi Kao, Teng-Yok Lee, Pradeep Sen, Ming-Yu Liu, "Localization-Aware Active Learning for Object Detection", arXiv:1801.0512v1, Jan 2018.

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