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Poster: TheDet: A Machine Learning-based Privacy-preserving Occupancy Estimation Method
RISE.
RISE.ORCID iD: 0000-0001-7257-4386
Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Electrical Engineering, Networked Embedded Systems. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Architecture and Computer Communication. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Computer Systems. RISE.ORCID iD: 0000-0002-2586-8573
RISE; KTH.
2022 (English)In: EWSN '22: Proceedings of the 2022 International Conference on Embedded Wireless Systems and Networks / [ed] Alois Ferscha, Mun Choon Chan, Salil Kanhere & Ranga Rao Venkatesha Prasad, New York: Association for Computing Machinery (ACM), 2022, p. 208-209Conference paper, Poster (with or without abstract) (Refereed)
Abstract [en]

Energy provision for buildings accounts for 40% of the total energy consumption in the EU, according to the European Union 2020 Energy Policy Review. Therefore, indoor occupancy estimation has attracted much attention in recent years as an important component of indoor energy management systems. However, existing methods commonly suffer from some limitations. For example, methods use conventional cameras which is generally not privacy-preserving or multiple environmental sensors which increases cost and maintenance efforts. On the other hand, multiple object detection (MOD) and multiple object tracking (MOT) have been extensively studied in recent years as important topics in the computer vision field. In this paper, we present TheDet, a privacy-preserving method that uses a lowresolution thermal camera together with MOD and MOT techniques to perform occupancy estimation.

Place, publisher, year, edition, pages
New York: Association for Computing Machinery (ACM), 2022. p. 208-209
National Category
Computer Systems Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:uu:diva-500874OAI: oai:DiVA.org:uu-500874DiVA, id: diva2:1753610
Conference
International Conference on Embedded Wireless Systems and Networks (EWSN '22), Linz, Austria, October 3-5, 2022
Funder
Swedish Foundation for Strategic ResearchAvailable from: 2023-04-27 Created: 2023-04-27 Last updated: 2023-09-04Bibliographically approved

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Eriksson, JoakimVoigt, Thiemo

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