Open set domain adaptation and universal domain adaptation for enhancing activity recognition in smart buildings
Room 6
August 26, 4:30 pm-4:45 pm
Smart buildings provide a solution for global energy issues by intelligent automation improving sustainability. Accurate Activity Recognition (AR) plays a key role in energy management through enabling adaptive control. This paper proposes new Open
Set Domain Adaptation (OSDA) and Universal Domain Adaptation (UniDA) methods aimed to address the differences due to data variation and lack of labeled training data in changing environments. These methods robustly differentiate between known and
unknown activities and thus generalize well to other domains. The experimental results show good classification accuracy and robust unknown detection of activities, which validates the feasibility of these approaches for real-world smart buildings.
Presenters
Nizar Bouguila
Universidad de Zaragoza