目标检测中的背景建模方法

目标检测中背景建模方法
背景建模或前景检测的算法主要有：
1. Single Gaussian (单高斯模型)
Real-time tracking of the human body
2. 混合高斯模型（Mixture of Gaussian Model）
An improved adaptive background mixture model for real-time tracking with shadow detection 3. 滑动高斯平均（Running Gaussian average）---Single Gaussian
Real-time tracking of the human body
4. 码本(CodeBook)
Real-time foreground–background segmentation using codebook model
Real-time foreground-background segmentation using a modified codebook model
5. 自组织背景检测( SOBS-Self-organization background subtraction)
A self-Organizing approach to background subtraction for+visual surveillance
6. 样本一致性背景建模算法(SACON)
A consensus-based method for tracking
A consensus-based method for tracking-Modelling background scenario and foreground appearance
SACON-Background subtraction based on a robust consensus method
7. VIBE算法
ViBe-A Universal Background Subtraction
8. 基于颜色信息的背景建模方法（Color)
A statistical approach for real-time robust background subtraction and shadow detection
9. 统计平均法
10. 中值滤波法( Temporal Median filter)
Automatic congestion detection system for underground platform
Detecting moving objects,ghost,and shadows in video streams
11. W4方法
12. 本征背景法
A Bayesian computer vision system for modeling human interactions
13. 核密度估计方法
Non-parametric model for background subtraction。