3D user-perspective, voxel-based estimation of visual focus of attention in dynamic meeting scenarios

In this paper we present a new framework for the online estimation of people's visual focus of attention from their head poses in dynamic meeting scenarios. We describe a voxel based approach to reconstruct the scene composition from an observer's perspective, in order to integrate occlusion handling and visibility verification. The observer's perspective is thereby simulated with live head pose tracking over four far-field views from the room's upper corners. We integrate motion and speech activity as further scene observations in a Bayesian Surprise framework to model prior attractors of attention within the situation's context. As evaluations on a dedicated dataset with 10 meeting videos show, this allows us to predict a meeting participant's focus of attention correctly in up to 72.2% of all frames

Camera-based estimation of student's attention in class

Two essential elements of classroom lecturing are the teacher and the students. This human core can easily be lost in the overwhelming list of technological supplements aimed at improving the teaching/learning experience. We start from the question of whether we can formulate a technological intervention around the human connection, and find indicators which would tell us when the teacher is not reaching the audience. Our approach is based on principles of unobtrusive measurements and social signal processing. Our assumption is that students with different levels of attention will display different non-verbal behaviour during the lecture. Inspired by information theory, we formulated a theoretical background for our assumptions around the idea of synchronization between the sender and receiver, and between several receivers focused on the same sender. Based on this foundation we present a novel set of behaviour metrics as the main contribution. By using a camera-based system to observe lectures, we recorded an extensive dataset in order to verify our assumptions. In our first study on motion, we found that differences in attention are manifested on the level of audience movement synchronization. We formulated the measure of ``motion lag'' based on the idea that attentive students would have a common behaviour pattern. For our second set of metrics we explored ways to substitute intrusive eye-tracking equipment in order to record gaze information of the entire audience. To achieve this we conducted an experiment on the relationship between head orientation and gaze direction. Based on acquired results we formulated an improved model of gaze uncertainty than the ones currently used in similar studies. In combination with improvements on head detection and pose estimation, we extracted measures of audience head and gaze behaviour from our remote recording system. From the collected data we found that synchronization between student's head orientation and teacher's motion serves as a reliable indicator of the attentiveness of students. To illustrate the predictive power of our features, a supervised-learning model was trained achieving satisfactory results at predicting student's attention