Behavior Analysis

Human activity analysis is an important part of ambient intelligence and computer vision. Its goal is to automatically analyze ongoing activities from one or multiple unknown video streams, which can then be correctly classified into a set of activities. Many applications are already available to support people in carrying out their everyday life activities and tasks, such as automatic light control, elderly care, meeting analysis (smart meetings), etc.

In this context, making use of low-level data, such as positional data for each meeting attendant or detailed face analysis, could help high-level analysis to understand, describe and explore the dynamics in meetings (smart meetings). Here, activities range from events, like "who is talking'' or "who is looking at who'' to more complex ones such as "who is the main speaker'', "who is paying attention in the meeting, who does not'', ...

However, the detection of such activities is still very challenging. Low-level data can be corrupted or imprecise due to environmental changes and inherent errors of the employed algorithms. Therefore, low-level data cannot be assumed to be correct or very precise at all times.

In this research, we focus on an approach to understand the dynamics in meetings using a multi-camera setup, consisting of fixed ambient and portable close-up cameras. Here, fixed ambient cameras are used, for example, to track meeting attendees or to observe a certain area in the meeting such as a white board or a screen. On the contrary, portable close-up cameras, such as laptop cameras, usually have only one specific participant in the field of view. These cameras can be used for detailed face analysis, but are also susceptible to small movements and therefore an on-line calibration process is needed.