A background subtraction algorithm for indoor monitoring surveillance systems

Mohamed Bachir Boubekeur; Senlin Luo; Hocine Labidi

doi:10.1109/CIVEMSA.2015.7158605

A background subtraction algorithm for indoor monitoring surveillance systems

Mohamed Bachir Boubekeur, Senlin Luo, Hocine Labidi

School of Information and Electronics

Beijing Institute of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Citations (Scopus)

Abstract

The use of the gray level intensity is a common practice for most of background subtraction algorithms due to speed matters in real time applications, and performance related considerations, yet using the RGB color representation could increase the efficiency of object detection thus the accuracy of the algorithm increases. In this paper, a non-parametric background subtraction algorithm based on samples modeling, adaptive threshold, and color layers combination is presented. The proposed framework showed an increase in performances regarding the accuracy and the robustness of the detection in indoor situations. The presented performance analysis supports the robustness of the algorithm to gradual illumination changes and ghost artifact.

Original language	English
Title of host publication	2015 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications, CIVEMSA 2015
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781479960910
DOIs	https://doi.org/10.1109/CIVEMSA.2015.7158605
Publication status	Published - 14 Jul 2015
Event	2015 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications, CIVEMSA 2015 - Shenzhen, China Duration: 12 Jun 2015 → 14 Jun 2015

Publication series

Name	2015 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications, CIVEMSA 2015

Conference

Conference	2015 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications, CIVEMSA 2015
Country/Territory	China
City	Shenzhen
Period	12/06/15 → 14/06/15

Access to Document

10.1109/CIVEMSA.2015.7158605

Cite this

Boubekeur, M. B., Luo, S., & Labidi, H. (2015). A background subtraction algorithm for indoor monitoring surveillance systems. In 2015 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications, CIVEMSA 2015 Article 7158605 (2015 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications, CIVEMSA 2015). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CIVEMSA.2015.7158605

Boubekeur, Mohamed Bachir ; Luo, Senlin ; Labidi, Hocine. / A background subtraction algorithm for indoor monitoring surveillance systems. 2015 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications, CIVEMSA 2015. Institute of Electrical and Electronics Engineers Inc., 2015. (2015 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications, CIVEMSA 2015).

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title = "A background subtraction algorithm for indoor monitoring surveillance systems",

abstract = "The use of the gray level intensity is a common practice for most of background subtraction algorithms due to speed matters in real time applications, and performance related considerations, yet using the RGB color representation could increase the efficiency of object detection thus the accuracy of the algorithm increases. In this paper, a non-parametric background subtraction algorithm based on samples modeling, adaptive threshold, and color layers combination is presented. The proposed framework showed an increase in performances regarding the accuracy and the robustness of the detection in indoor situations. The presented performance analysis supports the robustness of the algorithm to gradual illumination changes and ghost artifact.",

author = "Boubekeur, {Mohamed Bachir} and Senlin Luo and Hocine Labidi",

note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 2015 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications, CIVEMSA 2015 ; Conference date: 12-06-2015 Through 14-06-2015",

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doi = "10.1109/CIVEMSA.2015.7158605",

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Boubekeur, MB, Luo, S & Labidi, H 2015, A background subtraction algorithm for indoor monitoring surveillance systems. in 2015 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications, CIVEMSA 2015., 7158605, 2015 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications, CIVEMSA 2015, Institute of Electrical and Electronics Engineers Inc., 2015 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications, CIVEMSA 2015, Shenzhen, China, 12/06/15. https://doi.org/10.1109/CIVEMSA.2015.7158605

A background subtraction algorithm for indoor monitoring surveillance systems. / Boubekeur, Mohamed Bachir; Luo, Senlin; Labidi, Hocine.
2015 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications, CIVEMSA 2015. Institute of Electrical and Electronics Engineers Inc., 2015. 7158605 (2015 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications, CIVEMSA 2015).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Y1 - 2015/7/14

N2 - The use of the gray level intensity is a common practice for most of background subtraction algorithms due to speed matters in real time applications, and performance related considerations, yet using the RGB color representation could increase the efficiency of object detection thus the accuracy of the algorithm increases. In this paper, a non-parametric background subtraction algorithm based on samples modeling, adaptive threshold, and color layers combination is presented. The proposed framework showed an increase in performances regarding the accuracy and the robustness of the detection in indoor situations. The presented performance analysis supports the robustness of the algorithm to gradual illumination changes and ghost artifact.

AB - The use of the gray level intensity is a common practice for most of background subtraction algorithms due to speed matters in real time applications, and performance related considerations, yet using the RGB color representation could increase the efficiency of object detection thus the accuracy of the algorithm increases. In this paper, a non-parametric background subtraction algorithm based on samples modeling, adaptive threshold, and color layers combination is presented. The proposed framework showed an increase in performances regarding the accuracy and the robustness of the detection in indoor situations. The presented performance analysis supports the robustness of the algorithm to gradual illumination changes and ghost artifact.

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Boubekeur MB, Luo S, Labidi H. A background subtraction algorithm for indoor monitoring surveillance systems. In 2015 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications, CIVEMSA 2015. Institute of Electrical and Electronics Engineers Inc. 2015. 7158605. (2015 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications, CIVEMSA 2015). doi: 10.1109/CIVEMSA.2015.7158605

A background subtraction algorithm for indoor monitoring surveillance systems

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