SuperBE: computationally light background estimation with superpixels

This paper presents a motion-based superpixel-level background estimation algorithm that aims to be competitively accurate while requiring less computation time for background modelling and updating. Superpixels are chosen for their spatial and colour coherency and can be grouped together to better...

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Published in:	Journal of real-time image processing Vol. 16; no. 6; pp. 2319 - 2335
Main Authors:	Chen, Andrew Tzer-Yeu, Biglari-Abhari, Morteza, Wang, Kevin I-Kai
Format:	Journal Article
Language:	English
Published:	Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2019 Springer Nature B.V
Subjects:	Accuracy Algorithms Batch processing Color Computer Graphics Computer Science Cost control Covariance matrix Embedded systems Image Processing and Computer Vision Multimedia Information Systems Original Research Paper Pattern Recognition Signal,Image and Speech Processing Surveillance Computer vision Image motion analysis Background estimation Real-time systems Embedded vision Object segmentation
ISSN:	1861-8200, 1861-8219
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Abstract	This paper presents a motion-based superpixel-level background estimation algorithm that aims to be competitively accurate while requiring less computation time for background modelling and updating. Superpixels are chosen for their spatial and colour coherency and can be grouped together to better define the shapes of objects in an image. RGB mean and colour covariance matrices are used as the discriminative features for comparing superpixels to their background model samples. The background model initialisation and update procedures are inspired by existing approaches, with the key aim of minimising computational complexity and therefore processing time. Experiments carried out with a widely used dataset show that SuperBE can achieve a high level of accuracy and is competitive against other state-of-the-art background estimation algorithms. The main contribution of this paper is the computationally efficient use of superpixels in background estimation while maintaining high accuracy, reaching 135 fps on 320 × 240 resolution images.
AbstractList	This paper presents a motion-based superpixel-level background estimation algorithm that aims to be competitively accurate while requiring less computation time for background modelling and updating. Superpixels are chosen for their spatial and colour coherency and can be grouped together to better define the shapes of objects in an image. RGB mean and colour covariance matrices are used as the discriminative features for comparing superpixels to their background model samples. The background model initialisation and update procedures are inspired by existing approaches, with the key aim of minimising computational complexity and therefore processing time. Experiments carried out with a widely used dataset show that SuperBE can achieve a high level of accuracy and is competitive against other state-of-the-art background estimation algorithms. The main contribution of this paper is the computationally efficient use of superpixels in background estimation while maintaining high accuracy, reaching 135 fps on 320 × 240 resolution images. This paper presents a motion-based superpixel-level background estimation algorithm that aims to be competitively accurate while requiring less computation time for background modelling and updating. Superpixels are chosen for their spatial and colour coherency and can be grouped together to better define the shapes of objects in an image. RGB mean and colour covariance matrices are used as the discriminative features for comparing superpixels to their background model samples. The background model initialisation and update procedures are inspired by existing approaches, with the key aim of minimising computational complexity and therefore processing time. Experiments carried out with a widely used dataset show that SuperBE can achieve a high level of accuracy and is competitive against other state-of-the-art background estimation algorithms. The main contribution of this paper is the computationally efficient use of superpixels in background estimation while maintaining high accuracy, reaching 135 fps on 320 × 240 resolution images.
Author	Biglari-Abhari, Morteza Wang, Kevin I-Kai Chen, Andrew Tzer-Yeu
Author_xml	– sequence: 1 givenname: Andrew Tzer-Yeu surname: Chen fullname: Chen, Andrew Tzer-Yeu email: andrew.chen@auckland.ac.nz organization: Embedded Systems Research Group, Department of Electrical and Computer Engineering, The University of Auckland – sequence: 2 givenname: Morteza surname: Biglari-Abhari fullname: Biglari-Abhari, Morteza organization: Embedded Systems Research Group, Department of Electrical and Computer Engineering, The University of Auckland – sequence: 3 givenname: Kevin I-Kai surname: Wang fullname: Wang, Kevin I-Kai organization: Embedded Systems Research Group, Department of Electrical and Computer Engineering, The University of Auckland
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SubjectTerms	Accuracy Algorithms Batch processing Color Computer Graphics Computer Science Cost control Covariance matrix Embedded systems Image Processing and Computer Vision Multimedia Information Systems Original Research Paper Pattern Recognition Signal,Image and Speech Processing Surveillance
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Title	SuperBE: computationally light background estimation with superpixels
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