Background/Foreground Separation: Guided Attention based Adversarial Modeling (GAAM) versus Robust Subspace Learning Methods

Maryam Sultana, Arif Mahmood, Thierry Bouwmans, Muhammad Haris Khan, Soon Ki Jung

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

Background-Foreground separation and appearance generation is a fundamental step in many computer vision applications. Existing methods like Robust Subspace Learning (RSL) suffer performance degradation in the presence of challenges like bad weather, illumination variations, occlusion, dynamic backgrounds and intermittent object motion. In the current work we propose a more accurate deep neural network based model for background-foreground separation and complete appearance generation of the foreground objects. Our proposed model, Guided Attention based Adversarial Model (GAAM), can efficiently extract pixel-level boundaries of the foreground objects for improved appearance generation. Unlike RSL methods our model extracts the binary information of foreground objects labeled as attention map which guides our generator network to segment the foreground objects from the complex background information. Wide range of experiments performed on the benchmark CDnet2014 dataset demonstrate the excellent performance of our proposed model.

Original languageEnglish
Title of host publicationProceedings - 2021 IEEE/CVF International Conference on Computer Vision Workshops, ICCVW 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages181-188
Number of pages8
ISBN (Electronic)9781665401913
DOIs
StatePublished - 2021
Event18th IEEE/CVF International Conference on Computer Vision Workshops, ICCVW 2021 - Virtual, Online, Canada
Duration: 11 Oct 202117 Oct 2021

Publication series

NameProceedings of the IEEE International Conference on Computer Vision
Volume2021-October
ISSN (Print)1550-5499

Conference

Conference18th IEEE/CVF International Conference on Computer Vision Workshops, ICCVW 2021
Country/TerritoryCanada
CityVirtual, Online
Period11/10/2117/10/21

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