Driver Attention Monitoring System using IBM Cloud
Sathya T1, Reshi Krish T2, Monish Kumar MS3, KS Preetha4
1Sathya T*, School of Electronics Engineering (SENSE), B. Tech, Electronice and Communication Engineering with Specialization in Internet of Things and Sensors, Vellore Institute of Technology, Vellore, India.
2Reshi Krish T, School of Electronics Engineering (SENSE), B. Tech, ECE with specialization in Internet of Things and Sensors, Vellore Institute of Technology, Vellore, India.
3Monish Kumar MS, School of Electronics Engineering (SENSE), B. Tech, ECE with specialization in Internet of Things and Sensors, Vellore Institute of Technology, Vellore, India.
4Dr. KS Preetha, Department of Communication Engineering, School of Electronics Engineering (SENSE), Senior Assistant Professor, Vellore Institute of Technology, Vellore, India.
Manuscript received on March 15, 2020. | Revised Manuscript received on March 29, 2020. | Manuscript published on April 10, 2020. | PP:1567-1574 | Volume-9 Issue-6, April 2020. | Retrieval Number: F4633049620/2020©BEIESP | DOI: 10.35940/ijitee.F4633.049620
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: With online shopping and many logistic companies on the rise, a single accident can incur heavy loss to the supply chain department and not only disrupts the flow of the supply chain, but also causes injury to life and damage to property. These accidents occur primarily due to driving while feeling distracted or drowsy and it is paramount to monitor such behavior to avoid drastic outcomes in case of driving heavy duty vehicles. Therefore, it is natural for logistic companies to invest in securing their goods and ensuring that there is safe transportation of goods. The objective of our paper is to provide a novel solution to handle the aforementioned problems by monitoring the driver’s performance by analysing the facial features of the driver in real-time while storing the event-triggered data in the cloud and using the cloud services to send mobile alerts when the driver is drowsy or distracted via a mobile application in a cost effective and in an efficient manner.
Keywords: Facial Feature Recognition, D Lib, Solve PnP, IBM Watson IoT Platform (WIoTP), Node RED, IFTTT (If This Then That).
Scope of the Article: Cloud Computing