Local Geometrically Enriched Mixtures for Stable and Robust Human Tracking in Detecting Falls

Detecting a fall through visual cues is emerging as a hot research agenda for improving the independence of the elderly. However, the traditional motion-based algorithms are very sensitive to noise, reducing fall detection accuracy. Another approach is to efficiently localize and then track the foreground object followed by measurements that aid the identification of a fall. However, to perform robust and stable tracking over a long time is a challenging research aspect in computer vision society. In this paper, we introduce a stable human tracker able to efficiently cope with the trade-off between model stability (accurate tracking performance) and adaptability (model evolution to visual changes). In particular, we introduce local geometrically enriched mixture models for background modelling. Then, we incorporate iterative motion information methods, constrained by shape and time properties, to estimate high confidence image regions for background model updating. This way, we are able to detect and track the foreground objects even when visual conditions are dynamically changed over time (luminosity or background/foreground changes or active cameras).