Projects per year
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 (IEEE) |
| Pages | 249-254 |
| Number of pages | 6 |
| ISBN (Print) | 978-1-4577-0845-9 |
| DOIs | |
| Publication status | Published - Sep 2011 |
| Event | 8th IEEE International Conference on Advanced Video and Signal-Based Surveillance - Klagenfurt, Austria Duration: 30 Aug 2011 → 02 Sep 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
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Dive into the research topics of 'Regularized Online Mixture of Gaussians for Background Subtraction'. Together they form a unique fingerprint.Projects
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R1118ECI: Centre for Secure Information Technologies (CSIT)
McCanny, J. V., Cowan, C., Crookes, D., Fusco, V., Linton, D., Liu, W., Miller, P., O'Neill, M., Scanlon, W. & Sezer, S.
01/08/2009 → 30/06/2014
Project: Research