Particle Filter Algorithm for Visual Tracking Based on MCD and Partial Linear Gaussian(LG) Models 基于MCD和局部线性高斯(LG)模型的视频跟踪粒子滤波算法
Target tracking is a typical dynamic system state estimation problem. When the system is linear Gaussian conditions, the Kalman filter can give the best estimation. 目标跟踪问题是典型的动态系统状态估计问题,在系统为线性、高斯条件下,卡尔曼滤波可以给出最优估计。
This paper substituted the linear Gaussian scale space with a non-linear wavelet-based one, obtaining the edge detection in the wavelet-based scale space. 提出用一个非线性的小波尺度空间代替高斯尺度空间,得到小波尺度空间中的边缘检测算法。
Begin with the Bayesian iterative estimation; we introduce the Kalman Filter which is the optimal solution in linear gaussian model. Then, we review the existing nonlinear filtering algorithms, and the meaning of the Monte Carlo method is also made clear. 从贝叶斯的迭代估计入手,介绍了线性高斯(LG)下的最优解:卡尔曼滤波,回顾了现有的一些非线性滤波算法。
The famous Kalman filtering algorithm is the best bayesian recursive estimator for solving linear Gaussian for more than 40 years. 著名的卡尔曼滤波算法从四十多年前被提出以来一直是用来解决线性高斯(LG)环境的最佳递推贝叶斯估计器。