Improved adaptive Gaussian mixture model for background subtraction
Z. Zivkovic
- Vol. 2, pp 28-31
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TLDR
An efficient adaptive algorithm using Gaussian mixture probability density is developed using Recursive equations to constantly update the parameters and but also to simultaneously select the appropriate number of components for each pixel.Abstract:
Background subtraction is a common computer vision task. We analyze the usual pixel-level approach. We develop an efficient adaptive algorithm using Gaussian mixture probability density. Recursive equations are used to constantly update the parameters and but also to simultaneously select the appropriate number of components for each pixel.read more
Citations
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A texture-based method for modeling the background and detecting moving objects
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A comprehensive review of background subtraction algorithms evaluated with synthetic and real videos
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References
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Proceedings ArticleDOI
Adaptive background mixture models for real-time tracking
Chris Stauffer,W.E.L. Grimson +1 more
TL;DR: This paper discusses modeling each pixel as a mixture of Gaussians and using an on-line approximation to update the model, resulting in a stable, real-time outdoor tracker which reliably deals with lighting changes, repetitive motions from clutter, and long-term scene changes.
Journal ArticleDOI
Pfinder: real-time tracking of the human body
TL;DR: Pfinder is a real-time system for tracking people and interpreting their behavior that uses a multiclass statistical model of color and shape to obtain a 2D representation of head and hands in a wide range of viewing conditions.
Book ChapterDOI
Non-parametric Model for Background Subtraction
TL;DR: A novel non-parametric background model that can handle situations where the background of the scene is cluttered and not completely static but contains small motions such as tree branches and bushes is presented.
Proceedings ArticleDOI
Wallflower: principles and practice of background maintenance
TL;DR: This work develops Wallflower, a three-component system for background maintenance that is shown to outperform previous algorithms by handling a greater set of the difficult situations that can occur.
Proceedings ArticleDOI
Pfinder: real-time tracking of the human body
TL;DR: Pfinder uses a multi-class statistical model of color and shape to obtain a 2-D representation of head and hands in a wide range of viewing conditions, useful for applications such as wireless interfaces, video databases, and low-bandwidth coding.