Regularized Online Mixture of Gaussians for Background Subtraction

Hongbin Wang; Paul Miller

doi:10.1109/AVSS.2011.6027331

Regularized Online Mixture of Gaussians for Background Subtraction

Hongbin Wang, Paul Miller

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

17 Citations (Scopus)

568 Downloads (Pure)

Abstract

Mixture of Gaussians (MoG) modelling [13] is a popular approach to background subtraction in video sequences. Although the algorithm shows good empirical performance, it lacks theoretical justification. In this paper, we give a justification for it from an online stochastic expectation maximization (EM) viewpoint and extend it to a general framework of regularized online classification EM for MoG with guaranteed convergence. By choosing a special regularization function, l1 norm, we derived a new set of updating equations for l1 regularized online MoG. It is shown empirically that l1 regularized online MoG converge faster than the original online MoG .

Original language	English
Title of host publication	Advanced Video and Signal-Based Surveillance (AVSS), 2011 8th IEEE International Conference on
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	249-254
Number of pages	6
ISBN (Print)	978-1-4577-0845-9
DOIs	https://doi.org/10.1109/AVSS.2011.6027331
Publication status	Published - Sept 2011
Event	8th IEEE International Conference on Advanced Video and Signal-Based Surveillance - Klagenfurt, Austria Duration: 30 Aug 2011 → 02 Sept 2011

Conference

Conference	8th IEEE International Conference on Advanced Video and Signal-Based Surveillance
Country/Territory	Austria
City	Klagenfurt
Period	30/08/2011 → 02/09/2011

ASJC Scopus subject areas

Computer Networks and Communications
Signal Processing

Access to Document

10.1109/AVSS.2011.6027331

Avss2011 final5

R1118ECI: Centre for Secure Information Technologies (CSIT)
McCanny, J. V. (PI), Cowan, C. (CoI), Crookes, D. (CoI), Fusco, V. (CoI), Linton, D. (CoI), Liu, W. (CoI), Miller, P. (CoI), O'Neill, M. (CoI), Scanlon, W. (CoI) & Sezer, S. (CoI)
01/08/2009 → 30/06/2014
Project: Research

Cite this

@inproceedings{142bdc4322c448c5bbb71370a2d54263,

title = "Regularized Online Mixture of Gaussians for Background Subtraction",

abstract = "Mixture of Gaussians (MoG) modelling [13] is a popular approach to background subtraction in video sequences. Although the algorithm shows good empirical performance, it lacks theoretical justification. In this paper, we give a justification for it from an online stochastic expectation maximization (EM) viewpoint and extend it to a general framework of regularized online classification EM for MoG with guaranteed convergence. By choosing a special regularization function, l1 norm, we derived a new set of updating equations for l1 regularized online MoG. It is shown empirically that l1 regularized online MoG converge faster than the original online MoG .",

author = "Hongbin Wang and Paul Miller",

year = "2011",

month = sep,

doi = "10.1109/AVSS.2011.6027331",

language = "English",

isbn = "978-1-4577-0845-9",

pages = "249--254",

booktitle = "Advanced Video and Signal-Based Surveillance (AVSS), 2011 8th IEEE International Conference on",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

address = "United States",

note = "8th IEEE International Conference on Advanced Video and Signal-Based Surveillance ; Conference date: 30-08-2011 Through 02-09-2011",

}

Wang, H & Miller, P 2011, Regularized Online Mixture of Gaussians for Background Subtraction. in Advanced Video and Signal-Based Surveillance (AVSS), 2011 8th IEEE International Conference on. Institute of Electrical and Electronics Engineers Inc., pp. 249-254, 8th IEEE International Conference on Advanced Video and Signal-Based Surveillance, Klagenfurt, Austria, 30/08/2011. https://doi.org/10.1109/AVSS.2011.6027331

TY - GEN

T1 - Regularized Online Mixture of Gaussians for Background Subtraction

AU - Wang, Hongbin

AU - Miller, Paul

PY - 2011/9

Y1 - 2011/9

N2 - Mixture of Gaussians (MoG) modelling [13] is a popular approach to background subtraction in video sequences. Although the algorithm shows good empirical performance, it lacks theoretical justification. In this paper, we give a justification for it from an online stochastic expectation maximization (EM) viewpoint and extend it to a general framework of regularized online classification EM for MoG with guaranteed convergence. By choosing a special regularization function, l1 norm, we derived a new set of updating equations for l1 regularized online MoG. It is shown empirically that l1 regularized online MoG converge faster than the original online MoG .

AB - Mixture of Gaussians (MoG) modelling [13] is a popular approach to background subtraction in video sequences. Although the algorithm shows good empirical performance, it lacks theoretical justification. In this paper, we give a justification for it from an online stochastic expectation maximization (EM) viewpoint and extend it to a general framework of regularized online classification EM for MoG with guaranteed convergence. By choosing a special regularization function, l1 norm, we derived a new set of updating equations for l1 regularized online MoG. It is shown empirically that l1 regularized online MoG converge faster than the original online MoG .

U2 - 10.1109/AVSS.2011.6027331

DO - 10.1109/AVSS.2011.6027331

M3 - Conference contribution

SN - 978-1-4577-0845-9

SP - 249

EP - 254

BT - Advanced Video and Signal-Based Surveillance (AVSS), 2011 8th IEEE International Conference on

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 8th IEEE International Conference on Advanced Video and Signal-Based Surveillance

Y2 - 30 August 2011 through 2 September 2011

ER -

Regularized Online Mixture of Gaussians for Background Subtraction

Abstract

Conference

ASJC Scopus subject areas

Access to Document

Fingerprint

Projects

R1118ECI: Centre for Secure Information Technologies (CSIT)

Cite this