2026-06-05T15:48:27Zhttps://riuma.uma.es/rest/oai/request

oai:riuma.uma.es:10630/163242026-02-03T12:12:02Zcom_10630_2254col_10630_37959

00925njm 22002777a 4500 dc Benito-Picazo, Jesús author Domínguez-Merino, Enrique author Palomo-Ferrer, Esteban José author López-Rubio, Ezequiel author Ortiz-de-Lazcano-Lobato, Juan Miguel author 2018 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 recognition in 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 to detect 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, which represents 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 on a Raspberry Pi microcontroller-based board, which enables the design and implementation of a low-cost monitoring system that is able to perform image processing. https://hdl.handle.net/10630/16324 Redes neuronales (Informática) Deep learning-based anomalous object detection system powered by microcontroller for PTZ cameras