In this paper we give a preview of our system for automatically evaluating attention in the classroom. We demonstrate our current behaviour metrics and preliminary observations on how they reflect the reactions of people to the given lecture. We also introduce foundations of our hypothesis on peripheral awareness of students during lectures.

Body language is an essential source of information in everyday communication. Low signal-to-noise ratio prevents us from using it in the automatic processing of student behaviour, an obstacle that we are slowly overcoming with advanced statistical methods. Instead of profiling individual behaviour of students in the classroom, the idea is to compare students and connect the observed traits to different levels of attention. With the usage of novel techniques from the field of computer vision, we focus on features that can be automatically extracted with a system of cameras, by means of passive observation of the classroom population. We show parallels between our work and previous theories and formulate a new concept for measuring the level of attention based on synchronization of student body movement. We observed that students with lower levels of attention are slower to react than focused students, a phenomenon we named "sleepers' lag." This realization may give rise to novel measurements that can act as a technological support for teacher metacognition. The goal is to improve the teacher-student conversation and to propose techniques that can enable a shorter feedback loop of the teacher's performance compared to the current-day methods.

Body language is an essential source of information in everyday communication. Low signal-to-noise ratio prevents us from using it in the automatic processing of student behaviour, an obstacle that we are slowly overcoming with advanced statistical methods. Instead of profiling individual behaviour of students in the classroom, the idea is to compare students and connect the observed traits to different levels of attention. With the usage of novel techniques from the field of computer vision, we focus on features that can be automatically extracted with a system of cameras, by means of passive observation of the classroom population. We show parallels between our work and previous theories and formulate a new concept for measuring the level of attention based on synchronization of student body movement. We observed that students with lower levels of attention are slower to react than focused students, a phenomenon we named "sleepers' lag." This realization may give rise to novel measurements that can act as a technological support for teacher metacognition. The goal is to improve the teacher-student conversation and to propose techniques that can enable a shorter feedback loop of the teacher's performance compared to the current-day methods.

We present our efforts towards building an observational system for measuring classroom activity. The goal is to explore visual cues which can be acquired with a system of video cameras and automatically processed to enrich the teacher's perception of the audience. The paper will give a brief overview of our methodology, explored features, and current findings.

Should we judge the quality of the class by the grades the students and teacher get at the end of the semester or how the group collaborated during the semester towards acquiring new knowledge? Up until recently, the later approach was all too inaccessible due to complexity and time needed to evaluate every class. With the development of new technologies in different branches of video processing, gaze tracking and audio analysis we are getting the opportunity to go further with our analysis and go around the potential problem substitution into which we were previously forced.We present our efforts to record student-student and studentteacher interactions within a classroom eco-system. For this purpose, we developed a multi-camera system for observing teacher actions and students reactions throughout the class. We complemented the data with a mobile eye-tracker worn by the teacher, quantitative questionnaire data collection, as well as in-depth interviews with students about their impressions of the classes they took, and about our intervention. The seven-part experiment was conducted during the autumn semester of 2013, in two classes with over 60 participants.We present the conclusions we reached about the experiment format, visualize the preliminary results of our processing and discuss other options we are considering for our further experiments. We aim to explore further possibilities for analysing classroom life in order to create a more responsive environment to the needs of the students.

This paper is motivated by the objective of improving the realism of real-time simulated crowds by reducing short term collision avoidance through long term anticipation of pedestrian trajectories. For this aim, we choose to reuse outdoor pedestrian trajectories obtained with non-invasive means. This initial step is achieved by analyzing the recordings of multiple synchronized video cameras. In a second off-line stage, we fit as long as possible trajectory segments within predefined paths made of a succession of region goals. The concept of region goal is exploited to enforce the principle of "sufficient satisfaction": it allows the pedestrians to relax the prescribed trajectory to the traversal of successive region goals. However, even if a fitted trajectory is modified due to collision avoidance, we are still able to make long-term trajectory anticipation and distribute the collision avoidance shift over a long distance.

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