Abstract
Background subtraction is an important first step for video analysis, where it is used to discover the objects of interest for further processing. Such an algorithm often consists of a background model and a regularisation scheme. The background model determines a per-pixel measure of if a pixel belongs to the background or the foreground, whilst the regularisation brings in information from adjacent pixels. A new method is presented that uses a Dirichlet process Gaussian mixture model to estimate a per-pixel background distribution, which is followed by probabilistic regularisation. Key advantages include inferring the per-pixel mode count, such that it accurately models dynamic backgrounds, and that it updates its model continuously in a principled way.
| Original language | English |
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| Pages | 99-113 |
| Number of pages | 15 |
| Publication status | Published - 2012 |
| Event | 12th European Conference on Computer Vision,2012 - Florence, Italy Duration: 7 Oct 2012 → 13 Oct 2012 |
Conference
| Conference | 12th European Conference on Computer Vision,2012 |
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| Country/Territory | Italy |
| City | Florence |
| Period | 7/10/12 → 13/10/12 |