Conference Proceeding

Automatic Robust Background Modeling Using Multivariate Non-parametric Kernel Density Estimation for Visual Surveillance.

01/2005; DOI:10.1007/11595755_44 In proceeding of: Advances in Visual Computing, First International Symposium, ISVC 2005, Lake Tahoe, NV, USA, December 5-7, 2005, Proceedings
Source: DBLP

ABSTRACT The final goal for many visual surveillance systems is auto- matic understanding of events in a site. Higher level processing on video data requires certain lower level vision tasks to be performed. One of these tasks is the segmentation of video data into regions that corre- spond to objects in the scene. Issues such as automation, noise robust- ness, adaptation, and accuracy of the model must be addressed. Current background modeling techniques use heuristics to build a representation of the background, while it would be desirable to obtain the background model automatically. In order to increase the accuracy of modeling it needs to adapt to different parts of the same scene and finally the model has to be robust to noise. The building block of the model representation used in this paper is multivariate non-parametric kernel density estima- tion which builds a statistical model for the background of the video scene based on the probability density function of its pixels. A post pro- cessing step is applied to the background model to achieve the spatial consistency of the foreground objects.

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