Video summarization for remote invigilation of online exams

This paper focuses on video summarization of abnormal behavior for remote invigilation of online exams. While the last decade has seen a massive increase in e-learning and online courses offered at postsecondary institutions, preserving the integrity of online examinations still heavily relies on web video conference invigilation performed by a remote proctor. Live remote invigilation is limited in the number of students that can be handled at once, and manual post-exam review is labor intensive. We propose a novel computer vision-based video content analysis system for the automatic creation of video summaries of online exams to assist remote proctors in post-exam reviews. The proposed method models normal and abnormal student behavior patterns using head pose estimations and a semantically meaningful two-state hidden Markov model. Video summaries are created from detected sequences of abnormal behavior. Experimental results are promising and demonstrate the viability of the proposed approach, which could readily be expanded to generate real-time alerts for live remote invigilation.