RT Conference Proceedings
T1 Foreground object detection enhancement by adaptive super resolution for video surveillance
A1 Molina-Cabello, Miguel Ángel
A1 Elizondo Acuña, David Alberto
A1 Luque-Baena, Rafael Marcos
A1 López-Rubio, Ezequiel
K1 Redes neuronales (Informática)
AB Foreground object detection is a fundamental low level task in current video surveillance systems. It is usually accomplished by keeping a model of the background at each frame pixel. Many background learning algorithms have difficulties to attain real time operation when applied directly to the output of state of the art high resolution surveillance cameras, due to the large number of pixels. Here we propose a strategy to address this problem which consists in maintaining a low resolution model of the background which is upscaled by adaptive super resolution in order to produce a foreground detection mask of the same size as the original input frame. Extensive experimental results demonstrate the suitability of our proposal, in terms of reduction of the computational load and foreground detection accuracy.
YR 2019
FD 2019-09-16
LK https://hdl.handle.net/10630/18348
UL https://hdl.handle.net/10630/18348
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