Poster abstract: camera-assisted training of non-vision sensors for anomaly detection

Albazzai, Norah, Rana, Omer ORCID: https://orcid.org/0000-0003-3597-2646 and Perera, Charith ORCID: https://orcid.org/0000-0002-0190-3346 2023. Poster abstract: camera-assisted training of non-vision sensors for anomaly detection. Presented at: 8th ACM/IEEE Conference on Internet of Things Design and Implementation, New York, NY, United States, 9-12 May 2023. Proceedings of the 8th ACM/IEEE Conference on Internet of Things Design and Implementation. Association for Computing Machinery, pp. 452-453. 10.1145/3576842.3589164

Full text not available from this repository.

Abstract

Cameras are becoming pervasive and used for image classification and object detection in various applications, including anomaly detection. However, cameras pose a privacy threat and require significant power resources. To address these issues, researchers have explored non-vision sensors, but pre-training them for anomaly detection is challenging because anomalies are difficult to define and vary significantly across indoor environments. Thus, we propose a new approach to training non-vision sensors using a tiny camera and a pre-trained MobileNetV2 model. Data from non-vision sensors are labelled based on the image classification from the tiny camera, and an anomaly detection model is trained using these labelled data. The Random Forest model is used as the final model, achieving an accuracy of 95.58%.

Item Type:	Conference or Workshop Item (Poster)
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics
Publisher:	Association for Computing Machinery
ISBN:	9798400700378
Last Modified:	11 May 2023 11:01
URI:	https://orca.cardiff.ac.uk/id/eprint/159354

Actions (repository staff only)

Edit Item