Assessment of Audience Understanding during Online Sessions Using Computer Vision

Develop a real-time computer vision solution to assess the understanding of the explained material by the audience during an online session (lecture, conference), by analyzing their faces, emotional response and/or pose based on their webcam video feeds.

Required interest(s)

  • Face Recognition
  • Applied Deep Learning
  • Human-Centered and Community-Minded Information Systems

What do you get

  • A challenging assignment within a practical environment
  • € 1000 compensation, € 500 + lease car or € 600 + living space
  • Professional guidance
  • Courses aimed at your graduation period
  • Support from our academic Research center at your disposal
  • Two vacation days per month

What you will do

  • 65% Research
  • 10% Analyze, design, realize
  • 25% Documentation

Nowadays, video conferencing has become more ubiquitous than ever before. This is especially true after the global lockdown prompted by the COVID-19 pandemic, during which work meetings, university lectures, school lessons, conferences, and even social activities, were moved to the realm of online video calls.

While this has posed several challenges and limitations compared to in-person gatherings, it has also offered many advantages and new possibilities. Concretely, the fact that every participant could stream a video of themselves during such encounters, entails a very powerful data source for video analytics and affective computing.

An essential task during these sessions, and school and university lectures in particular, is the assessment of the general understanding by the audience. While a teacher can often intuitively assess this during in-person lessons, it is much harder to do so during online lessons; especially if there is a high number of students, or if the teacher only has one screen available for their streaming.

Face and pose analysis techniques –such as facial expression or emotion recognition, face action units detection, pose estimation, or eye-gaze estimation– could be used to model human understanding, and potentially applied to automatically detect and rate the level of comprehension among an online audience in video sessions. Having such insights could help teachers and lecturers find out possible weak points in their explanations, assess the content difficulty, discover knowledge gaps among their students or audience, and further improve their lessons.

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