Journal ArticleDOI
A texture-based method for modeling the background and detecting moving objects
Marko Heikkilä,Matti Pietikäinen +1 more
TLDR
A novel and efficient texture-based method for modeling the background and detecting moving objects from a video sequence that provides many advantages compared to the state-of-the-art.Abstract:
This paper presents a novel and efficient texture-based method for modeling the background and detecting moving objects from a video sequence. Each pixel is modeled as a group of adaptive local binary pattern histograms that are calculated over a circular region around the pixel. The approach provides us with many advantages compared to the state-of-the-art. Experimental results clearly justify our model.read more
Citations
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Journal ArticleDOI
A survey of advances in vision-based human motion capture and analysis
TL;DR: This survey reviews recent trends in video-based human capture and analysis, as well as discussing open problems for future research to achieve automatic visual analysis of human movement.
Journal ArticleDOI
Description of interest regions with local binary patterns
TL;DR: A new texture feature called center-symmetric local binary pattern (CS-LBP) is introduced that is a modified version of the well-known localbinary pattern (LBP), and is computationally simpler than the SIFT.
Journal ArticleDOI
Local Derivative Pattern Versus Local Binary Pattern: Face Recognition With High-Order Local Pattern Descriptor
TL;DR: The nth-order LDP is proposed to encode the (n-1)th -order local derivative direction variations, which can capture more detailed information than the first-order local pattern used in local binary pattern (LBP).
Journal ArticleDOI
Local Binary Patterns and Its Application to Facial Image Analysis: A Survey
TL;DR: As a typical application of the LBP approach, LBP-based facial image analysis is extensively reviewed, while its successful extensions, which deal with various tasks of facial imageAnalysis, are also highlighted.
Journal ArticleDOI
Traditional and recent approaches in background modeling for foreground detection: An overview
TL;DR: The purpose of this paper is to provide a complete survey of the traditional and recent approaches to background modeling for foreground detection, and categorize the different approaches in terms of the mathematical models used.
References
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Journal ArticleDOI
Multiresolution gray-scale and rotation invariant texture classification with local binary patterns
TL;DR: A generalized gray-scale and rotation invariant operator presentation that allows for detecting the "uniform" patterns for any quantization of the angular space and for any spatial resolution and presents a method for combining multiple operators for multiresolution analysis.
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
A comparative study of texture measures with classification based on featured distributions
TL;DR: This paper evaluates the performance both of some texture measures which have been successfully used in various applications and of some new promising approaches proposed recently.
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.
Proceedings ArticleDOI
Improved adaptive Gaussian mixture model for background subtraction
TL;DR: 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.