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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Vydáno v:Journal of real-time image processing Ročník 16; číslo 6; s. 2319 - 2335
Hlavní autoři: Chen, Andrew Tzer-Yeu, Biglari-Abhari, Morteza, Wang, Kevin I-Kai
Médium: Journal Article
Jazyk:angličtina
Vydáno: Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2019
Springer Nature B.V
Témata:
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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Shrnutí: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.
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ISSN:1861-8200
1861-8219
DOI:10.1007/s11554-018-0750-7