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[SourceCode] Particle filters DL : 0: 粒子滤波，解决非线性非高斯的利器
Update : 2011-03-22 Size : 150.42kb Publisher : lgjsw@126.com
[Special Effects] visualtracker DL : 0: Video Tracker Version 0.0.1 "Visual tracking and recognition using appearance-adaptive models in particle filters" (IEEE transactions on Image Processing, Nov 2004) by Shaohua Zhou, Rama Chellappa and Baback Moghaddam. -Video Tracker Version 0.0.1 "Visual track ing and recognition using appearance-adaptiv e models in particle filters "(IEEE transactio ns on Image Processing, Nov 2004) by Zhou Shaohua. Rama Chellappa and Baback Moghaddam.
Update : 2025-02-17 Size : 3.41mb Publisher : 薛斌
[Applications] bfl-0.4.2 DL : 0: Klaas Gadeyne, a Ph.D. student in the Mechanical Engineering Robotics Research Group at K.U.Leuven, has developed a C++ Bayesian Filtering Library that includes software for Sequential Monte Carlo methods, Kalman filters, particle filters, etc. -Klaas Gadeyne. a Ph.D. student in the Mechanical Engineering R obotics Research Group at K. U. Leuven, C has developed a Bayesian Filtering Library th at includes software for Sequential Monte Carl o methods, Kalman filters, particle filters, etc..
Update : 2025-02-17 Size : 417kb Publisher : 江河
[File Format] ParticleFilterTrackerwithIsomap DL : 0: We propose a novel approach for head tracking, which combines particle filters with Isomap. The particle filter works on the low-dimensional embedding of training images. It indexes into the Isomap with its state variables to find the closest template for each particle. The most weighted particle approximates the location of head. We develop a synthetic video sequence to test our technique. The results we get show that the tracker tracks the head which changes position, poses and lighting conditions. -We propose a novel approach for head tracking, which combines particle filters with Isomap. The particle filter works on the low-dimensional embedding of training images. It indexes into the Isomap with its state variables to find the closest template for each particle. The most weighted particle approximates the location of head. We develop a synthetic video sequence to test our technique. The results we get show that the tracker tracks the head which changes position, poses and lighting conditions.
Update : 2025-02-17 Size : 172kb Publisher : 阳关
[File Format] AMODIFIEDRAO-BLACKWELLISEDPARTICLEFILTER DL : 0: Rao-Blackwellised Particle Filters (RBPFs) are a class of Particle Filters (PFs) that exploit conditional dependencies between parts of the state to estimate. By doing so, RBPFs can improve the estimation quality while also reducing the overall computational load in comparison to original PFs. However, the computational complexity is still too high for many real-time applications. In this paper, we propose a modified RBPF that requires a single Kalman Filter (KF) iteration per input sample. Comparative experiments show that while good convergence can still be obtained, computational efficiency is always drastically increased, making this algorithm an option to consider for real-time implementations.
Update : 2025-02-17 Size : 119kb Publisher : 阳关
[Special Effects] BPF_1_3 DL : 0: 用粒子滤波器实现的多运动员跟踪，包含了data文件-Using Particle Filters athletes realize the multi-tracking, contains a data file
Update : 2025-02-17 Size : 3.23mb Publisher : huangmu
[Special Effects] AtutorialonparticlefiltersforonlinenonlinearnonGau DL : 0: A Tutorial on Particle Filters for Online Nonlinear/Non-Gaussian Bayesian Tracking
Update : 2025-02-17 Size : 321kb Publisher : 88txj
[Other] Real-timeParticleFilters DL : 0: we present real-time particle filters, which make use of all sensor information even when the filter update rate is below the update rate of the sensors.
Update : 2025-02-17 Size : 182kb Publisher : 黄松
[Other] TheUnscentedParticleFilter DL : 0: 1. The Unscented Transformation and UKF 2. Applications of UT/UKF to Particle Filters
Update : 2025-02-17 Size : 599kb Publisher : 黄松
[Other] ATutorialonParticleFilters DL : 0: A Tutorial on Particle Filters！-A Tutorial on Particle Filters!
Update : 2025-02-17 Size : 322kb Publisher : 罗云峰
[MPI] dysii-1.4.0.tar DL : 0: dysii is a C++ library for distributed probabilistic inference and learning in large-scale dynamical systems. It provides methods such as the Kalman, unscented Kalman, and particle filters and smoothers, as well as useful classes such as common probability distributions and stochastic processes. -dysii is a C library for distributed probabilistic inference and learning in large-scale dynamical systems. It provides methods such as the Kalman, unscented Kalman, and particle filters and smoothers, as wel
Update : 2025-02-17 Size : 184kb Publisher : xz
[matlab] KFandPF DL : 0: 卡尔曼滤波器和粒子滤波器的MATLAB演示程序-Kalman filters and particle filters MATLAB demo
Update : 2025-02-17 Size : 7kb Publisher : xuxin
[Software Engineering] mcl DL : 0: 详细介绍MCL算法，是由Sebastian Thrun a, Dieter Fox, Wolfram Burgard, Frank Dellaert所著的论文，发表于Artificial Intelligence上。-Mobile robot localization is the problem of determining a robot’s pose from sensor data. This article presents a family of probabilistic localization algorithms known as Monte Carlo Localization [MCL]. MCL algorithms represent a robot’s belief by a set of weighted hypotheses [samples], which approximate the posterior under a common Bayesian formulation of the localization problem. Building on the basic MCL algorithm, this article develops a more robust algorithm called Mixture- MCL, which integrates two complimentary ways of generating samples in the estimation. To apply this algorithm to mobile robots equipped with range finders, a kernel density tree is learned that permits fast sampling. Systematic empirical results illustrate the robustness and computational efficiency of the approach.  2001 Published by Elsevier Science B.V. Keywords: Mobile robots Localization Position estimation Particle filters Kernel density trees
Update : 2025-02-17 Size : 1.36mb Publisher : xuyuhua
[matlab] RSRBootstrap DL : 0: 自举粒子滤波器和改进后的自举粒子滤波器的比较应用-Bootstrap particle filters and improved bootstrap comparison of the application of particle filters
Update : 2025-02-17 Size : 250kb Publisher : 王军
[Other] TaIoPF DL : 0: Theory and Implementation of Particle Filters
Update : 2025-02-17 Size : 360kb Publisher : phong duong
[matlab] code1 DL : 0: the attached file consists of matlab code for implementation of sequential importance sampling particle filter given in IEEE paper entitled as "A TUTORIAL ON PARTICLE FILTERS FOR ONLINE NONLINEAR NON GUASSIAN BAYESIAN TRACKING"
Update : 2025-02-17 Size : 1kb Publisher : babi
[Special Effects] particale_filters DL : 0: 粒子滤波器是通过蒙特卡罗模拟来实现递归贝叶斯滤波，它不需要线性、高斯噪声的假设，适用于任何能用状态空间模型表示的非线性系统，比卡尔曼滤波器的适用范围广。这里给出了几个粒子滤波的matlab编程实例。-Particle filters are using Monte Carlo simulations to achieve the recursive Bayesian filtering, it does not require linear, Gaussian noise assumptions, can be used for any state-space model of nonlinear systems .It has a wider scope application than the Kalman filter . Here are a few examples of particle filter matlab programming.
Update : 2025-02-17 Size : 11kb Publisher : 郑玉凤
[Special Effects] particle-filters- DL : 0: 一种用于非线性系统的粒子滤波课件，讲得很好-a tutorial particle filters for on-line nonlear
Update : 2025-02-17 Size : 518kb Publisher : qiangminli
[Other] Particle-Filters-for-Random-Set-Models DL : 0: Particle Filters for Random Set Models By: Branko Ristic -Particle Filters for Random Set Models By: Branko Ristic
Update : 2025-02-17 Size : 4.35mb Publisher : Gomaa Haroun
[Program doc] Branko-Ristic-(auth.)-Particle-Filters-for-Random DL : 0: Particle Filters for Random Set Models
Update : 2025-02-17 Size : 4.35mb Publisher : hossein

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