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Multimedia Systems
Published in: Multimedia Systems 6/2023

28-07-2023 | Regular Paper

A multi-scale feature fusion spatial–channel attention model for background subtraction

Authors: Yizhong Yang, Tingting Xia, Dajin Li, Zhang Zhang, Guangjun Xie

Published in: Multimedia Systems | Issue 6/2023

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Abstract

Background subtraction is an essential task in computer vision, and is often used as a pre-processing step for many advanced tasks. In this work, we propose a novel multi-scale feature fusion attention mechanism network to tackle cross-scene background subtraction. The cross-fusion of feature maps at different stages of the encoder makes the features input into the decoder contain low-level and high-level information. The spatial–channel attention based on the weight matrix makes the model focus on processing information related to foreground extraction. We evaluate the proposed model on the CDnet-2014 dataset with two scene-independent evaluation strategies and obtain competitive F-Measure. In addition, to evaluate the generalization ability of the model, we perform a cross-dataset evaluation scheme on the LASIESTA and SBI2015 datasets. The overall F-Measure of the model is 0.89 and 0.93, respectively. Experimental results demonstrate that the model performs well compared to the current state-of-the-art methods.
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Metadata
Title
A multi-scale feature fusion spatial–channel attention model for background subtraction
Authors
Yizhong Yang
Tingting Xia
Dajin Li
Zhang Zhang
Guangjun Xie
Publication date
28-07-2023
Publisher
Springer Berlin Heidelberg
Published in
Multimedia Systems / Issue 6/2023
Print ISSN: 0942-4962
Electronic ISSN: 1432-1882
DOI
https://doi.org/10.1007/s00530-023-01139-1

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