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Unsupervised Adversarial Learning for Dynamic Background Modeling

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Frontiers of Computer Vision (IW-FCV 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1212))

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Abstract

Dynamic Background Modeling (DBM) is a crucial task in many computer vision based applications such as human activity analysis, traffic monitoring, surveillance, and security. DBM is extremely challenging in scenarios like illumination changes, camouflage, intermittent object motion or shadows. In this study, we proposed an end-to-end framework based on Generative Adversarial Network, which can generate dynamic background information for the task of DBM in an unsupervised manner. Our proposed model can handle the problem of DBM in the presence of the challenges mentioned above by generating data similar to the desired information. The primary aim of our proposed model during training is to learn all the dynamic changes in a scene-specific background information. While, during testing, inverse mapping of data to latent space representation in our model generates dynamic backgrounds similar to test data. The comparative analysis of our proposed model upon experimental evaluations on SBM.net and SBI benchmark datasets has outperformed eight existing methods for DBM in many challenging scenarios.

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Notes

  1. 1.

    http://scenebackgroundmodeling.net/.

  2. 2.

    http://sbmi2015.na.icar.cnr.it/SBIdataset.html.

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Acknowledgements

This research was supported by Development project of leading technology for future vehicle of the business of Daegu metropolitan city (No. 20171105).

Also this study was supported by the BK21 Plus project (SW Human Resource Development Program for Supporting Smart Life) funded by the Ministry of Education, School of Computer Science and Engineering, Kyungpook National University, Korea (21A20131600005).

Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, Kyungpook National University, Daegu, South Korea

    Maryam Sultana & Soon Ki Jung

  2. Department of Computer Science, Information Technology University (ITU), Lahore, Pakistan

    Arif Mahmood

  3. Laboratoire MIA, Universite deLaRochelle, LaRochelle, France

    Thierry Bouwmans

Authors
  1. Maryam Sultana
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  2. Arif Mahmood
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  3. Thierry Bouwmans
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  4. Soon Ki Jung
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Corresponding author

Correspondence to Soon Ki Jung .

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Editors and Affiliations

  1. Saitama Institute of Technology, Saitama, Japan

    Wataru Ohyama

  2. Kyungpook National University, Daegu, Korea (Republic of)

    Soon Ki Jung

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Sultana, M., Mahmood, A., Bouwmans, T., Jung, S.K. (2020). Unsupervised Adversarial Learning for Dynamic Background Modeling. In: Ohyama, W., Jung, S. (eds) Frontiers of Computer Vision. IW-FCV 2020. Communications in Computer and Information Science, vol 1212. Springer, Singapore. https://doi.org/10.1007/978-981-15-4818-5_19

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  • DOI: https://doi.org/10.1007/978-981-15-4818-5_19

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