Foreground Objects Detection Using a Fully Convolutional Network With a Background Model Image and Multiple Original Images

Foreground Objects Detection Using a Fully Convolutional Network With a Background Model Image and Multiple Original Images

Visual surveillance aims to reliably extract foreground objects. Traditional algorithms usually use a background model image which is generated through the probabilistic modeling of changes over time and space. They detect foreground objects by comparing a background model image with a current image...

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Bibliographic Details
Main Authors: Jae-Yeul Kim, Jong-Eun Ha
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Foreground objects detection
visual surveillance
generalization power
deep learning
fully convolutional network
Online Access:https://ieeexplore.ieee.org/document/9183919/

Internet

https://ieeexplore.ieee.org/document/9183919/

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