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Adaptive background estimation using intensity independent features
 Proc. British Machine Vision Conference
, 2006
"... The problem of subtracting the background in an image sequence is central to computer vision. The general idea is to model the values of each pixel as a random variable. This approach has proved to be efficient when treating slowly varying changes or changes that are fairly periodic. In this paper w ..."
Abstract

Cited by 4 (3 self)
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The problem of subtracting the background in an image sequence is central to computer vision. The general idea is to model the values of each pixel as a random variable. This approach has proved to be efficient when treating slowly varying changes or changes that are fairly periodic. In this paper we propose a novel method for background and foreground estimation that efficiently handles changes that also occur with non periodicity and fast. Furthermore, the method makes only very mild assumptions about the scene making it able to operate in a wide variety of conditions. This is done by introducing a novel set of invariants that are independent to the over all intensity level in the images. By using these features instead of the raw pixel data we automatically obtain a background estimator that is insensitive to rapid changes in lighting conditions. Furthermore, the features can be computed very efficiently using the so called integral image. Inspired by the work in [17] we update the probability model over time to make it able to handle new objects entering the background, but here we work directly with the histogram which reduces the execution time considerably. Finally, we present test data that shows that the model works well in some outdoor scenes. In particular it is shown that it can handle difficult outdoor scenes with rapidly bypassing clouds. 1
Background Subtraction Using Ensembles of Classifiers with an Extended Feature Set
, 2008
"... Background subtraction using ensembles of classifiers with an extended feature set ..."
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Background subtraction using ensembles of classifiers with an extended feature set
Foreground Estimation and Hidden Markov Models for Tracking
, 2005
"... We will give a short introduction to foreground/background estimation and Hidden Markov for tracking. More information about the topics can be found in the papers listed at the end. 1 Foreground estimation The objective is to extract the foreground and consequently also the background from a sequenc ..."
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We will give a short introduction to foreground/background estimation and Hidden Markov for tracking. More information about the topics can be found in the papers listed at the end. 1 Foreground estimation The objective is to extract the foreground and consequently also the background from a sequence of images. Problems facing us includes for example • long execution time, • slowly varying lighting conditions, • rapidly varying lighting conditions, and • what should be considered background. The image sequence may come from a video camera with 352 × 288 resolution color images running 20 frames per second. Let It: R 2 → R 3, t = 0,... n−1 be a sequence of n color images. We use the notation It = (I 1 t, I 2 t, I 3 t) to denote the different color channels when needed. In order to compute a feature at each location we can use convolution f j t (x, y) = Ij t ∗ h(x, y) = I j t (x − a, y − b)h(a, b)dadb, where h: R 2 → R is the filter mask. This gives a filter response at every point (x, y) ∈ R 2 and the statistical properties of these can be used to classify background and foreground. 1.1 Pixel based foreground estimation The Stauffer–Grimson [15] estimator is obtained by letting h = δ0,0 be the Dirac measure at the origin in which case I j t ∗ δ0,0 = I j t, i.e. that is the estimator is based on the individual pixel data. A simple solution would be to define a probability function at each point (x, y) ∈ R. Note that for digital images there are only a finite set of points in the definition set giving a finite set of probability functions. Thus, for a gray level image we will need to define probability functions px,y(a) like for example px,y(a) = 1 √ e
1 Adaptive Background Estimation using Intensity Independent Features
"... The problem of subtracting the background in an image sequence is central to computer vision. The general idea is to model the values of each pixel as a random variable. This approach has proved to be efficient when treating slowly varying changes or changes that are fairly periodic. In this paper w ..."
Abstract
 Add to MetaCart
(Show Context)
The problem of subtracting the background in an image sequence is central to computer vision. The general idea is to model the values of each pixel as a random variable. This approach has proved to be efficient when treating slowly varying changes or changes that are fairly periodic. In this paper we propose a novel method for background and foreground estimation that efficiently handles changes that also occur with non periodicity and fast. Furthermore, the method makes only very mild assumptions about the scene making it able to operate in a wide variety of conditions. This is done by introducing a novel set of invariants that are independent to the over all intensity level in the images. By using these features instead of the raw pixel data we automatically obtain a background estimator that is insensitive to rapid changes in lighting conditions. Furthermore, the features can be computed very efficiently using the so called integral image. Inspired by the work in [17] we update the probability model over time to make it able to handle new objects entering the background, but here we work directly with the histogram which reduces the execution time considerably. Finally, we present test data that shows that the model works well in some outdoor scenes. In particular it is shown that it can handle difficult outdoor scenes with rapidly bypassing clouds. 1