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Video foreground detection based on symmetric alpha-stable mixture models.

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Video foreground detection based on symmetric alpha-stable mixture models. / Bhaskar, H.; Mihaylova, Lyudmila; Achim, A.
In: IEEE Transactions on Circuits and Systems for Video Technology, Vol. 20, No. 8, 08.2010, p. 1133-1138.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Harvard

Bhaskar, H, Mihaylova, L & Achim, A 2010, 'Video foreground detection based on symmetric alpha-stable mixture models.', IEEE Transactions on Circuits and Systems for Video Technology, vol. 20, no. 8, pp. 1133-1138. https://doi.org/10.1109/TCSVT.2010.2051282

APA

Bhaskar, H., Mihaylova, L., & Achim, A. (2010). Video foreground detection based on symmetric alpha-stable mixture models. IEEE Transactions on Circuits and Systems for Video Technology, 20(8), 1133-1138. https://doi.org/10.1109/TCSVT.2010.2051282

Vancouver

Bhaskar H, Mihaylova L, Achim A. Video foreground detection based on symmetric alpha-stable mixture models. IEEE Transactions on Circuits and Systems for Video Technology. 2010 Aug;20(8):1133-1138. doi: 10.1109/TCSVT.2010.2051282

Author

Bhaskar, H. ; Mihaylova, Lyudmila ; Achim, A. / Video foreground detection based on symmetric alpha-stable mixture models. In: IEEE Transactions on Circuits and Systems for Video Technology. 2010 ; Vol. 20, No. 8. pp. 1133-1138.

Bibtex

@article{93ef59830d014db48a8a843962115d36,
title = "Video foreground detection based on symmetric alpha-stable mixture models.",
abstract = "Background subtraction (BS) is an efficient technique for detecting moving objects in video sequences. A simple BS process involves building a model of the background and extracting regions of the foreground (moving objects) with the assumptions that the camera remains stationary and there exist no movements in the background. These assumptions restrict the applicability of BS methods to real-time object detection in video. In this paper, we propose an extended cluster BS technique with a mixture of symmetric alpha stable (SS) distributions. An on-line self-adaptive mechanism is presented that allows automated estimation of the model parameters using the log moment method. Results over real video sequences from indoor and outdoor environments, with data from static and moving video cameras are presented. The SS mixture model is shown to improve the detection performance compared with a cluster BS method using a Gaussian mixture model and the method of Li et al. [11].",
keywords = "automatic object detection, background subtraction, segmentation, alpha stable distribution",
author = "H. Bhaskar and Lyudmila Mihaylova and A. Achim",
note = "{"}{\textcopyright}2010 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.{"} {"}This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.{"}",
year = "2010",
month = aug,
doi = "10.1109/TCSVT.2010.2051282",
language = "English",
volume = "20",
pages = "1133--1138",
journal = "IEEE Transactions on Circuits and Systems for Video Technology",
issn = "1051-8215",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "8",

}

RIS

TY - JOUR

T1 - Video foreground detection based on symmetric alpha-stable mixture models.

AU - Bhaskar, H.

AU - Mihaylova, Lyudmila

AU - Achim, A.

N1 - "©2010 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE." "This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder."

PY - 2010/8

Y1 - 2010/8

N2 - Background subtraction (BS) is an efficient technique for detecting moving objects in video sequences. A simple BS process involves building a model of the background and extracting regions of the foreground (moving objects) with the assumptions that the camera remains stationary and there exist no movements in the background. These assumptions restrict the applicability of BS methods to real-time object detection in video. In this paper, we propose an extended cluster BS technique with a mixture of symmetric alpha stable (SS) distributions. An on-line self-adaptive mechanism is presented that allows automated estimation of the model parameters using the log moment method. Results over real video sequences from indoor and outdoor environments, with data from static and moving video cameras are presented. The SS mixture model is shown to improve the detection performance compared with a cluster BS method using a Gaussian mixture model and the method of Li et al. [11].

AB - Background subtraction (BS) is an efficient technique for detecting moving objects in video sequences. A simple BS process involves building a model of the background and extracting regions of the foreground (moving objects) with the assumptions that the camera remains stationary and there exist no movements in the background. These assumptions restrict the applicability of BS methods to real-time object detection in video. In this paper, we propose an extended cluster BS technique with a mixture of symmetric alpha stable (SS) distributions. An on-line self-adaptive mechanism is presented that allows automated estimation of the model parameters using the log moment method. Results over real video sequences from indoor and outdoor environments, with data from static and moving video cameras are presented. The SS mixture model is shown to improve the detection performance compared with a cluster BS method using a Gaussian mixture model and the method of Li et al. [11].

KW - automatic object detection

KW - background subtraction

KW - segmentation

KW - alpha stable distribution

U2 - 10.1109/TCSVT.2010.2051282

DO - 10.1109/TCSVT.2010.2051282

M3 - Journal article

VL - 20

SP - 1133

EP - 1138

JO - IEEE Transactions on Circuits and Systems for Video Technology

JF - IEEE Transactions on Circuits and Systems for Video Technology

SN - 1051-8215

IS - 8

ER -