A particle filter based fusion framework for video-radio tracking in smart spaces

Abstract

One of the main issues for Ambient Intelligence (AmI) systems is to continuously localize the user and to detect his/her identity in order to provide dedicated services. A video-radio fusion methodology, relying on the Particle Filter algorithm, is here proposed to track objects in a complex extensive environment, exploiting the complementary benefits provided by both systems. Visual tracking commonly outperforms radio localization in terms of precision but it is inefficient because of occlusions and illumination changes. Instead, radio measurements, gathered by a user's radio device, are unambiguously associated to the respective target through the "virtual" identity (i.e. MAC/IP addresses). The joint usage of the two data typologies allows a more robust tracking and a major flexibility in the architectural setting up of the AmI system. The method has been extensively tested in a simulated and off-line framework and on real world data proving its effectiveness.