Moving object detection in noisy video sequences using deep convolutional disentangled representations

Autor:	García-González, Jorge, Luque-Baena, Rafael Marcos, Ortiz-de-lazcano-Lobato, Juan Miguel, López-Rubio, Ezequiel
Rok vydání:	2022
Předmět:	Foreground Segmentation Moving Object Detection Autoencoders Procesado de imágenes
Zdroj:	RIUMA. Repositorio Institucional de la Universidad de Málaga instname
Popis:	Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech. Noise robustness is crucial when approaching a moving de- tection problem since image noise is easily mistaken for movement. In order to deal with the noise, deep denoising autoencoders are commonly proposed to be applied on image patches with an inherent disadvantage with respect to the segmentation resolution. In this work, a fully convolutional autoencoder-based moving detection model is proposed in order to deal with noise with no patch extraction required. Different autoencoder structures and training strategies are also tested to get insights into the best network design ap- proach.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=RECOLECTA___::2eea89de35025ede0e04ec23e572029b https://hdl.handle.net/10630/25286 Zobrazit plný text záznamu