The Indian Institute of Technology, Ropar (IIT-Ropar) has developed an algorithm for driver drowsiness detection using machine learning and computer vision. A study titled ‘Machine Learning Models for Drowsiness Detection’ was recently jointly published by Harshit M and J.M.P. Ganesh from the Department of Mechanical Engineering and Ashish Sahani from the Centre for Bio-Medical Engineering.
The researchers said they used computer vision algorithms to extract facial features such as eye closure and yawning as well as machine learning techniques to effectively detect driver’s alertness. It is an industrial and academic challenge to develop drowsiness detection technologies. Multiple techniques have been developed in recent years, according to a news report.
One method is where the driver’s operation and vehicle behaviour can be monitored by the steering wheel movement, accelerator or brake patterns, vehicle speed, lateral acceleration, and lateral displacement. Another set of techniques focuses on monitoring the physiological characteristics of the driver such as heart rate, pulse rate, and electroencephalography. The third set is based on computer vision systems, which can recognise the facial changes occurring during drowsiness.
The first method is limited by the type and model of the car. The second method though with more accurate results has widely been downplayed due to the impracticality in deploying it on a large scale, as well as its intrusive nature. The third method is a very promising one, which the researchers have followed and developed a model on the same.
They have successfully developed the algorithm using hand-engineered features detecting drowsiness based on human facial expressions. The researchers were able to come up with an effective solution, with little inconvenience caused to the driver in the form of close-to-body sensors and instruments. Also, it will work efficiently irrespective of the model and age of the car. They have developed an algorithm to detect a drowsy state in real-time. Although the study said though the proposed machine learning-based detection can detect drowsiness with reasonable accuracy, there is still scope for improvement in its performance.
Recently, IIT-Ropar installed the Enterprise Resource Planning (ERP) software that was developed and extensively used by IIT-Kharagpur for institutional use. IIT Kharagpur has signed a memorandum of understanding (MoU) with the Institute for the customisation, installation, and deployment of the ERP system.
The indigenously developed software was deployed across all major functions of the Institute to improve organisational administration and shift operations to e-management, through both the intranet and Internet as well as desktops and mobiles. Close to 2,000 of the teaching and non-teaching staff and more than 14,000 students use the software regularly. Additionally, a large number of external people also access various modules for applications to its academic programs, guest house services, collaborators, etc., moving toward a self-reliant India (Atmanirbharta), according to a news report.
It is uniquely designed to provide circumspect support institutional function covering admissions, academics, placement, hostel management, the recruitment for faculty and non-faculty positions. As well as research and consultancy activities, accounts, institutional works, and guest house management. The necessary end-user customisation, training of staff to support operation, and further customisation are included within the scope of the MoU.
