Prethodno priopćenje

https://doi.org/10.17559/TV-20220417151954

Real-Time Building Management System Visual Anomaly Detection Using Heat Points Motion Analysis Machine Learning Algorithm

İsa Avci orcid.org/0000-0001-7032-8018 ; Karabuk University, Kastamonu Yolu Demir Çelik Kampüsü, 78050 Kılavuzlar/Karabük
Michael Bidollahkhani orcid.org/0000-0001-8122-4441 ; Karabuk University, Kastamonu Yolu Demir Çelik Kampüsü, 78050 Kılavuzlar/Karabük

Sažetak

The multiplicity of design, construction, and use of IoT devices in homes has made it crucial to provide secure and manageable building management systems and platforms. Increasing security requires increasing the complexity of the user interface and the access verification steps in the system. Today, multi-step verification methods are used via SMS, call, or e-mail to do this. Another topic mentioned here is physical home security and energy management. Artificial intelligence and machine learning-based tools and algorithms are used to analyze images and data from sensors and security cameras. However, these tools are not always available due to the increase in data volume over time and the need for large processing resources. In this study, a new method is proposed to reduce the usage of process resources and the percentage of system error in anomaly detection by reducing visual data to critical points by using thermal cameras. This method can also be used in energy management using home and ambient temperature and user activity measurements. The statistical results of the visual comparison between the proposed method and the legacy CCTV-based visual and sensory surveillance shown in the results section demonstrate its reliability and accuracy.

Ključne riječi

anomaly detection algorithm; building management system; digital image processing; internet of things; machine learning

Hrčak ID:

288432

URI

https://hrcak.srce.hr/288432

Datum izdavanja:

15.12.2022.

Posjeta: 721 *