NDLI: Foreground segmentation using multiscale convolutional neural network

Content Provider	IET Digital Library
Author	Fu, Hui Ni Wang, Ben Zhang Liu, Heng Zhu
Abstract	Foreground segmentation is to segment moving objects from a video or camera. Many approaches have been proposed to perform background subtraction. However, these algorithms could not handle complex scenes like illumination changes, dynamic background and camouflage etc. In this Letter, the authors propose a robust end-to-end convolutional neural network with multiscale features to address these problems. They use only a few training samples with RGB channels and output the corresponding foreground mask, which could save millions in labelling ground-truth for the big dataset. They evaluate their model using Change Detection 2014 dataset, and results outperformed other state-of-the-art methods with F-measure of 0.982 with ∼30 fps processing speed.
Starting Page	597
Ending Page	599
Page Count	3
ISSN	00135194
Volume Number	56
e-ISSN	1350911X
Issue Number	Issue 12, Jun (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/el/56/12
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/el.2019.3668
Journal	Electronics Letters
Publisher Date	2020-03-19
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Background Subtraction Change Detection 2014 Dataset Computer Vision Computer Vision And Image Processing Technique Convolutional Neural Nets Convolutional Neural Network Dynamic Background Foreground Mask Foreground Segmentation Illumination Changes Image Colour Analysis Image Segmentation Moving Object Segmentation Multiscale Convolutional Neural Network Neural Computing Technique Object Detection Optical, Image And Video Signal Processing RGB Channel Video Signal Processing
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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