2026-06-05T12:52:52Zhttps://riuma.uma.es/rest/oai/request

oai:riuma.uma.es:10630/303102026-02-03T12:22:14Zcom_10630_2254col_10630_37959

Benito-Picazo, Jesús Domínguez-Merino, Enrique Palomo-Ferrer, Esteban José Ramos-Jiménez, Gonzalo Pascual López-Rubio, Ezequiel 2024-02-09T12:56:01Z 2024-02-09T12:56:01Z 2021 J. Benito-Picazo, E. Domínguez, E. J. Palomo, G. Ramos-Jiménez and E. López-Rubio, "Deep learning-based anomalous object detection system for panoramic cameras managed by a Jetson TX2 board," 2021 International Joint Conference on Neural Networks (IJCNN), Shenzhen, China, 2021, pp. 1-7, doi: 10.1109/IJCNN52387.2021.9534053 https://hdl.handle.net/10630/30310 10.1109/IJCNN52387.2021.9534053 Social conflicts appearing in the media are increas ing public awareness about security issues, resulting in a higher demand of more exhaustive environment monitoring methods. Automatic video surveillance systems are a powerful assistance to public and private security agents. Since the arrival of deep learn ing, object detection and classification systems have experienced a large improvement in both accuracy and versatility. However, deep learning-based object detection and classification systems often require expensive GPU-based hardware to work properly. This paper presents a novel deep learning-based foreground anomalous object detection system for video streams supplied by panoramic cameras, specially designed to build power efficient video surveillance systems. The system optimises the process of searching for anomalous objects through a new potential detection generator managed by three different multivariant homoscedastic distributions. Experimental results obtained after its deployment in a Jetson TX2 board attest the good performance of the system, postulating it as a solvent approach to power saving video surveillance systems. eng open access Videovigilancia - Congresos Deep learning-based anomalous object detection system for panoramic cameras managed by a Jetson TX2 board conference output