Regularized Online Mixture of Gaussians for Background Subtraction
| Data(s) |
01/09/2011
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|---|---|
| Resumo |
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 . |
| Formato |
application/pdf |
| Identificador | |
| Idioma(s) |
eng |
| Publicador |
Institute of Electrical and Electronics Engineers (IEEE) |
| Direitos |
info:eu-repo/semantics/restrictedAccess |
| Fonte |
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 (IEEE) , pp. 249-254 , 8th IEEE International Conference on Advanced Video and Signal-Based Surveillance , Klagenfurt , Austria , 30-2 September . DOI: 10.1109/AVSS.2011.6027331 |
| Palavras-Chave | #/dk/atira/pure/subjectarea/asjc/1700/1705 #Computer Networks and Communications #/dk/atira/pure/subjectarea/asjc/1700/1711 #Signal Processing |
| Tipo |
contributionToPeriodical |
