RT Conference Proceedings
T1 Deep learning-based anomalous object detection system powered by microcontroller for PTZ cameras
A1 Benito-Picazo, Jesús
A1 Domínguez-Merino, Enrique
A1 Palomo-Ferrer, Esteban José
A1 López-Rubio, Ezequiel
A1 Ortiz-de-Lazcano-Lobato, Juan Miguel
K1 Redes neuronales (Informática)
AB Automatic video surveillance systems are usually designed to detect anomalous objects being present in a scene or behaving dangerously. In order to perform adequately, they must incorporate models able to achieve accurate pattern recognitionin an image, and deep learning neural networks excel at this task. However, exhaustive scan of the full image results in multiple image blocks or windows to analyze, which could make the time performance of the system very poor when implemented on low cost devices. This paper presents a system which attempts todetect abnormal moving objects within an area covered by a PTZ camera while it is panning. The decision about the block of the image to analyze is based on a mixture distribution composed of two components: a uniform probability distribution, whichrepresents a blind random selection, and a mixture of Gaussian probability distributions. Gaussian distributions represent windows in the image where anomalous objects were detected previously and contribute to generate the next window to analyze close to those windows of interest. The system is implemented ona Raspberry Pi microcontroller-based board, which enables the design and implementation of a low-cost monitoring system that is able to perform image processing.
PB IEEE
YR 2018
FD 2018
LK https://hdl.handle.net/10630/16324
UL https://hdl.handle.net/10630/16324
LA eng
NO Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech.
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 20 ene 2026