Towards monitoring human activities using an omnidirectional camera

Abstract

We propose an approach for monitoring human activities in an indoor environment using an omnidirectional camera. Robustly tracking people is prerequisite for modeling and recognizing human activities. An omnidirectional camera mounted on the ceiling is less prone to problems of occlusion. We use the Markov Random Field (MRF) to present both background and foreground, and adapt models effectively against environment changes. We employ a deformable model to adapt the foreground models to optimally match objects in different position within a pattern of view of the omnidirectional camera. In order to monitor human activity, we represent positions of people as spatial points and analyze moving trajectories within a time-spatial window. The method provides an efficient way to monitoring high-level human activities without exploring identities.