Local behavior modeling based on long-term tracking data

Modeling the behavior of elderly people to detect changes in their health status or mobility is challenging and thus requires to combine temporal and spatial knowledge. Spatial knowledge is obtained by a novel human centered scene understanding approach, being able to accurately model sitting and walking regions based on noisy long-term tracking data from a depth sensor, without exploiting geometric information. A local behavior model based on the detected functional regions is introduced, allowing an in depth behavioral analysis. The proposed approaches are evaluated on three different datasets from two application domains (home and office environment), containing more than 180 days of tracking data.