Identifying Distracted Students using Computer Vision and Statistical Methods

Effective interaction between student and teacher makes a class meaningful. While teachers try their best in delivering the lectures, it is the students who need to be checked from time to time. Computer vision and statistical approaches are being rigorously used for the automation of various activities in our day-to-day life. While one helps to process and analyze visual data, the latter helps in arriving at conclusions. Nowadays, it can be easily seen that these technologies are being successfully used in different areas like facial recognition, biometrics, self-driving vehicles, etc. Hence, in this paper, we propose an application, which is a unique combination of computer vision and statistical techniques that can help respective authorities to identify students who are not concentrating in a class, in real-time, or on prerecorded videos.