Suspicious human activity detection in classroom examination

Abstract

The proposed work aims in developing a system that analyze and detect the suspicious activity that are often occurring in a classroom environment. Video Analytics provides an optimal solution for this as it helps in pointing out an event and retrieves the relevant information from the video recorded. The system framework consists of three parts to monitor the student activity during examination. Firstly, the face region of the students is detected and monitored using Haar feature Extraction. Secondly, the hand contact detection is analyzed when two students exchange papers or any other foreign objects between them by grid formation. Thirdly, the hand signaling of the student using convex hull is recognized and the alert is given to the invigilator. The system is built using C/C++ and OpenCV library that shows the better performance in the real-time video frames.