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2012
IEEE

Background modeling using adaptive pixelwise kernel variances in a hybrid feature space

6 years 9 months ago
Background modeling using adaptive pixelwise kernel variances in a hybrid feature space
Recent work on background subtraction has shown developments on two major fronts. In one, there has been increasing sophistication of probabilistic models, from mixtures of Gaussians at each pixel [7], to kernel density estimates at each pixel [1], and more recently to joint domainrange density estimates that incorporate spatial information [6]. Another line of work has shown the benefits of increasingly complex feature representations, including the use of texture information, local binary patterns, and recently scale-invariant local ternary patterns [4]. In this work, we use joint domain-range based estimates for background and foreground scores and show that dynamically choosing kernel variances in our kernel estimates at each individual pixel can significantly improve results. We give a heuristic method for selectively applying the adaptive kernel calculations which is nearly as accurate as the full procedure but runs much faster. We combine these modeling improvements with rece...
Manjunath Narayana, Allen R. Hanson, Erik G. Learn
Added 28 Sep 2012
Updated 28 Sep 2012
Type Journal
Year 2012
Where CVPR
Authors Manjunath Narayana, Allen R. Hanson, Erik G. Learned-Miller
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