Fährmann, Daniel ; Boutros, Fadi ; Kubon, Philipp ; Kirchbuchner, Florian ; Kuijper, Arjan ; Damer, Naser (2023)
Ubiquitous multi-occupant detection in smart environments.
In: Neural Computing and Applications
doi: 10.1007/s00521-023-09162-z
Artikel, Bibliographie
Kurzbeschreibung (Abstract)
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.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2023 |
| Autor(en): | Fährmann, Daniel ; Boutros, Fadi ; Kubon, Philipp ; Kirchbuchner, Florian ; Kuijper, Arjan ; Damer, Naser |
| Art des Eintrags: | Bibliographie |
| Titel: | Ubiquitous multi-occupant detection in smart environments |
| Sprache: | Englisch |
| Publikationsjahr: | 27 November 2023 |
| Verlag: | Springer |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | Neural Computing and Applications |
| DOI: | 10.1007/s00521-023-09162-z |
| Kurzbeschreibung (Abstract): | 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. |
| Freie Schlagworte: | Smart environments, Multivariate time series, Machine learning |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Graphisch-Interaktive Systeme 20 Fachbereich Informatik > Mathematisches und angewandtes Visual Computing |
| Hinterlegungsdatum: | 04 Dez 2023 12:47 |
| Letzte Änderung: | 31 Jan 2024 07:43 |
| PPN: | 515148253 |
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