2026-05-28T08:53:36Zhttps://riuma.uma.es/rest/oai/requestoai:riuma.uma.es:10630/183482026-02-03T12:21:11Zcom_10630_2254col_10630_37959
RIUMA. Repositorio Institucional de la Universidad de Málaga
author
Molina-Cabello, Miguel Ángel
author
Elizondo Acuña, David Alberto
author
Luque-Baena, Rafael Marcos
author
López-Rubio, Ezequiel
2019-09-16T07:52:09Z
2019-09-16T07:52:09Z
2019-09-16
https://hdl.handle.net/10630/18348
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.
eng
Redes neuronales (Informática)
Foreground object detection enhancement by adaptive super resolution for video surveillance
conference output
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URL
https://riuma.uma.es/bitstreams/4165beba-26fb-40a8-91e8-b187a71b31e2/download
File
MD5
ed3e24fa7acf9fee760376f41caea7b4
484101
application/pdf
BMVC_2019___resize.pdf