Multimodal identification and localization of users in a smart environment
Detecting the location and identity of users is a first step in creating context-aware applications for technologically-endowed environments. We propose a system that makes use of motion detection, person tracking, face identification, feature-based identification, audio-based localization, and audio-based identification modules, fusing information with particle filters to obtain robust localization and identification. The data streams are processed with the help of the generic client-server middleware SmartFlow, resulting in a flexible architecture that runs across different platforms.
multimodal identification particle filters multimodal tracking smart rooms