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CVPR
2004
IEEE
11 years 6 months ago
Motion-Based Background Subtraction Using Adaptive Kernel Density Estimation
Background modeling is an important component of many vision systems. Existing work in the area has mostly addressed scenes that consist of static or quasi-static structures. When...
Anurag Mittal, Nikos Paragios
CVPR
2012
IEEE
8 years 6 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 Gaussi...
Manjunath Narayana, Allen R. Hanson, Erik G. Learn...
GI
2007
Springer
10 years 10 months ago
Background Modeling Using Adaptive Cluster Density Estimation for Automatic Human Detection
: Detection is an inherent part of every advanced automatic tracking system. In this work we focus on automatic detection of humans by enhanced background subtraction. Background s...
Harish Bhaskar, Lyudmila Mihaylova, Simon Maskell
PRL
2006
153views more  PRL 2006»
10 years 4 months ago
Efficient adaptive density estimation per image pixel for the task of background subtraction
We analyze the computer vision task of pixel-level background subtraction. We present recursive equations that are used to constantly update the parameters of a Gaussian mixture m...
Zoran Zivkovic, Ferdinand van der Heijden
CVPR
2006
IEEE
11 years 6 months ago
A Framework for Feature Selection for Background Subtraction
Background subtraction is a widely used paradigm to detect moving objects in video taken from a static camera and is used for various important applications such as video surveill...
Toufiq Parag, Ahmed M. Elgammal, Anurag Mittal
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