Description: 程序可以实现三维目标的跟踪监控,利用的是扩展卡尔曼滤波器EKF算法-Program can achieve the three-dimensional target tracking control, the use of the extended Kalman filter EKF algorithm Platform: |
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Author:张肖 |
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Description: In this implementation of tracking a ball, we will track a live ball using Kalman filter. The tracking will switch to autorun mode when the sight of the ball is lost and Kalman will estimate the motion based on it s previous states Platform: |
Size: 299008 |
Author:sofi |
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Description: 自己编写的一个循环增益卡尔曼滤波程序包,用于对机动目标进行检测和跟踪的滤波算法,给出目标数学模型和噪声模型,仿真后给出平均观测误差。程序里相应位置有标有注释。供做雷达机动目标检测和跟踪方面研究的人员参考。-I have written a loop-gain Kalman filter package, used for maneuvering target detection and tracking of the filter algorithm, given objective mathematical model and noise model of simulation, the average observation error is given. Procedures in place are marked with the corresponding notes. Do radar for maneuvering target detection and tracking studies for reference. Platform: |
Size: 12288 |
Author:王宗鑫 |
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Description: 目标跟踪是卡尔曼滤波的主要应用领域,通过本作业,进一步加深对卡尔曼滤波算法的理解,了解卡尔曼滤波器算法的基本特点,掌握卡尔曼滤波算法应用研究的基本步骤和方法。 -Kalman filter target tracking is the main application areas, through the operation, and further deepen the understanding of the Kalman filter algorithm to understand the basic characteristics of the Kalman filter algorithm, Kalman filter algorithm applied research to master the basic steps and methods. Platform: |
Size: 31744 |
Author:yufeng |
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Description: 这是一篇关于自适应卡尔曼滤波在目标跟踪系统中应用的学习文章,对学习卡尔曼滤波有很大的帮助-This is an article on adaptive Kalman filter target tracking system applied in the study article, to learn the Kalman filter is very helpful Platform: |
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Author:yang |
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Description: 用MATLAB编写的单目标跟踪算法程序,采用了递归式算法,包括极大似然然估计,卡尔曼滤波,扩展卡尔曼滤波和无迹卡尔曼滤波,带有注释,易于理解。-Written with the MATLAB program single-target tracking algorithm, using recursive algorithms, including maximum likelihood estimation, Kalman filtering, extended Kalman filter and unscented Kalman filter, with comments, easy to understand. Platform: |
Size: 11264 |
Author:asd |
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Description: 本系统中VIS欠缺的SIFT_VC.lib文件。。。
http://www.pudn.com/downloads224/sourcecode/math/detail1055031.html-This is lib file, which is used in Video Intelligent System (VIS) based on the Microsoft Visual Studio 2008 compiler environment and OpenCV 2.0 library. It includes foreground detection, motion object detection, motion object tracking, trajectories generation and analysis modules. It realizes a friendly interface based on dialog, which provides a convenient example for new learners.
keywords: opencv, mixture of gaussian model, sift feature and ransac method, mean shift, particle filter, kalman filter, object detection and tracking, video intelligent system. Platform: |
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Author: |
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Description: 提取图像中一个简单线性物体的特征并用卡尔曼滤波跟踪-Extracted image features of a simple linear objects and use Kalman filter tracking Platform: |
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Author:sunzhipeng |
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Description: EKF仅仅利用了非线性函数Taylor展开式的一阶偏导部分(忽略高阶项),常常导致在状态的后验分布的估计上产生较大的误差,影响滤波算法的性能,从而影响整个跟踪系统的性能。最近,在自适应滤波领域又出现了新的算法——无味变换Kalman滤波器(Unscented Kalman Filter-UKF)。UKF的思想不同于EKF滤波,它通过设计少量的σ点,由σ点经由非线性函数的传播,计算出随机向量一、二阶统计特性的传播。因此它比EKF滤波能更好地迫近状态方程的非线性特性,从而比EKF滤波具有更高的估计精度。
-EKF only uses non-linear function of the first-order Taylor expansion of some partial derivatives (ignoring higher order terms), often leading to the posterior distribution of the state estimates to generate large errors affect the performance of filtering algorithms, which affect the whole tracking system performance. Recently, the field of adaptive filtering algorithms and the emergence of new- and tasteless transform Kalman filter (Unscented Kalman Filter-UKF). EKF UKF filter is different from the idea that it points through the design of a small amount of σ by σ point spread through the nonlinear function to calculate the random vector first and second order statistical properties of the transmission. Therefore it is better than the EKF filter nonlinear characteristics equation of state approach, which is more than the EKF filter estimation accuracy. Platform: |
Size: 130048 |
Author:zyz |
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Description: 简单的图像追踪程序,运用卡尔曼滤波-背景差法实现对物体的追踪和标注-Simple image tracking process, the use of Kalman filter- background subtraction to achieve the tracking and labeling of objects Platform: |
Size: 699392 |
Author:caihan |
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Description: 该代码用于实现粒子滤波视觉目标跟踪(PF)、卡尔曼粒子滤波视觉目标跟踪(KPF)、无迹粒子滤波视觉目标跟踪(UPF)。它们是本人这两年来编写的核心代码,用于实现鲁棒的视觉目标跟踪,其鲁棒性远远超越MeanShift(均值转移)和Camshift之类。用于实现视觉目标跟踪的KPF和UPF都是本人花费精力完成,大家在网上是找不到相关代码的。这些代码虽然只做了部分代码优化,但其优化版本已经成功应用于我们研究组研发的主动视觉目标跟踪打击平台中。现在把它们奉献给大家!-These codes are used to realize particle filter based visual object tracking (PF), kalman particle filter based visual object tracking, unscented particle filter based visual object tracking. Their robustness is far beyond the classical visual object tracking algorithms such as Mean-Shift (MeanShift) and CamShift。The codes of KPF and UPF for visual object tracking cost a great of my energy, and you can not find any relating algorithm codes on internet! Our research group have optimized these codes and applied them to develop a platform for active visual object tracking. Now, I dedicate them to you and wish you love them! Platform: |
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Author:朱亮亮 |
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Description: Kalman filter toolbox written by Kevin Murphy, 1998.
See http://www.ai.mit.edu/~murphyk/Software/kalman.html for details.
Installation
------------
1. Install KPMtools from http://www.ai.mit.edu/~murphyk/Software/KPMtools.html
3. Assuming you installed all these files in your matlab directory, In Matlab type
addpath matlab/KPMtools
addpath matlab/Kalman
Demos
-----
See tracking_demo.m for a demo of 2D tracking.
See learning_demo.m for a demo of parameter estimation using EM.
-Kalman filter toolbox written by Kevin Murphy, 1998. See http://www.ai.mit.edu/ ~ murphyk/Software/kalman.html for details. Installation------------ 1. Install KPMtools from http://www.ai.mit.edu/ ~ murphyk/Software/KPMtools.html 3. Assuming you installed all these files in your matlab directory, In Matlab type addpath matlab/KPMtools addpath matlab/Kalman Demos---- See tracking_demo.m for a demo of 2D tracking. See learning_demo.m for a demo of parameter estimation using EM. Platform: |
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Author:张镖 |
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