|Fährmann, Daniel; Boutros, Fadi; Kubon, Philipp; Kirchbuchner, Florian; Kuijper, Arjan; Damer, Naser
|Recent advancements in ubiquitous computing have emphasized the need for privacy-preserving occupancy detection in smart environments to enhance security. This work presents a novel occupancy detection solution utilizing privacy-aware sensing technologies. The solution analyzes time-series data to detect not only occupancy as a binary problem, but also determines whether one or multiple individuals are present in an indoor environment. On three real-world datasets, our models outperformed various state-of-the-art algorithms, achieving F1-scores up to 94.91% in single-occupancy detection and a macro F1-score of 91.55% in multi-occupancy detection. This makes our approach a promising solution for improving security in smart environments.
|Secure Urban Infrastructures