Description: 该源码是关于运动对象跟踪的算法，主要实现了高斯背景建模，全局运动补偿（SIFT特征和RANSAC算法），运动对象检测，对象跟踪算法（Mean Shift，Particle Filter等），对象特征提取（轨迹，大小，起止帧等），同时，程序基于VC2008+OpenCV开发，实现了对话框式的程序界面，效率高。-This is a source about motion object tracking, including foreground modeling,object detection,object tracking,feature generation. It developed based on VC2008 and OpenCV library. The friendly interface is very convinient for new learners. Platform: |
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Author: |
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Description: A simple example showing how to track an object with particle filter. Likelihood is based on Bhattacharya distance of color histogram. Platform: |
Size: 13255680 |
Author:sofi |
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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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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: |
Size: 396288 |
Author:朱亮亮 |
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Description: This contained BG/FG detection(simple version and adaptive background mixture models), blob tracking(connected component tracking and MSPF resolver, mean shift, particle filter), Kalman filter using OpenCV. It can be helpful who studying object detection and tracking algorithm. Also, It contain the video clip for testing algorithm.-This is contained BG/FG detection(simple version and adaptive background mixture models), blob tracking(connected component tracking and MSPF resolver, mean shift, particle filter), Kalman filter using OpenCV. It can be helpful who studying object detection and tracking algorithm. Also, It contain the video clip for testing algorithm. Platform: |
Size: 52594688 |
Author:byunghee |
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Description: 该程序实现了粒子滤波算法，应用重要性重采样对实现对一维空间的非线性跟踪。使用MFC编程完成，效果很好，值得一试。-The codes realize the function of tracking an object with particle filter algorithm. And it is suitable for nonlinear system, with VC++ programming. It is really worth trial Platform: |
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Author:王中华 |
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Description: We present a parallel implementation of a histogram-based particle filter for object tracking on smart
cameras based on SIMD processors. We specifically focus on parallel computation of the particle weights
and parallel construction of the feature histograms since these are the major bottlenecks in standard
implementations of histogram-based particle filters. The proposed algorithm can be applied with any histogram-
based feature sets—we show in detail how the parallel particle filter can employ simple color histograms
as well as more complex histograms of oriented gradients (HOG). The algorithm was successfully
implemented on an SIMD processor and performs robust object tracking at up to 30 frames per second—a
performance difficult to achieve even on a modern desktop computer. Platform: |
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Author:gugu |
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Description: Particle filters are often used for tracking objects within a
scene. As the prediction model of a particle filter is often
implemented using basic movement predictions such as ran-
domwalk,constantvelocityoracceleration,thesemodelswill
usually be incorrect. Therefore, this paper proposes a new
approach, based on a Canonical Correlation Analysis (CCA)
tracking method which provides an object specific motion
model. This model is used to construct a proposal distribu-
tion of the prediction model which predicts new states, in-
creasing the robustness of the particle filter. Results confirm
anincreaseinaccuracycomparedtostate-of-the-artmethods. Platform: |
Size: 391168 |
Author:arkan |
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Description: Color can provide an efficient visual feature for tracking nonrigid
objects in real-time. However, the color of an object can vary over
time dependent on the illumination, the visual angle and the camera
parameters. To handle these appearance changes a color-based target
model must be adapted during temporally stable image observations.
This paper presents the integration of color distributions into particle
filtering and shows how these distributions can be adapted over time. A
particle filter tracks several hypotheses simultaneously and weights them
according to their similarity to the target model. As similarity measure
between two color distributions the popular Bhattacharyya coefficient is
applied. In order to update the target model to slowly varying image
conditions, frames where the object is occluded or too noisy must be
discarded. Platform: |
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Author:yangs |
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Description: 一种改进的粒子滤波算法的研究 粒子滤波基本原理，通过改进权重计算、重采样算法， 计算速度得到提高。改进的算法在DSP系统中进行目标跟踪仿真，证明其具有速度快、 精度高的特点-An improved study the basic principles of particle filter particle filter algorithm, recalculation by improving the rights, resampling algorithm to calculate the speed improved. Improved algorithms for object tracking simulation DSP system proved its high speed, high accuracy Platform: |
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Author:周玉晓 |
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Description: This paper introduces a new object tracking method
which combines two algorithms working in parallel, and based on
low-level observations (colour and gradient orientation): the Generalised Hough Transform, using a pixel-based description, and
the Particle Filter, using a global description. The object model is
updated by combining information a back-projection map
computed the Generalised Hough Transform, providing
for every pixel the degree to which it may belong to the
object, and the Particle Filter, providing a probability
density on the global object position. The proposed tracker
makes the most of the two algorithms, in terms of robustness to
appearance variation like scaling, rotation, non-rigid deformation
or illumination changes.-This paper introduces a new object tracking method
which combines two algorithms working in parallel, and based on
low-level observations (colour and gradient orientation): the Generalised Hough Transform, using a pixel-based description, and
the Particle Filter, using a global description. The object model is
updated by combining information a back-projection map
computed the Generalised Hough Transform, providing
for every pixel the degree to which it may belong to the
object, and the Particle Filter, providing a probability
density on the global object position. The proposed tracker
makes the most of the two algorithms, in terms of robustness to
appearance variation like scaling, rotation, non-rigid deformation
or illumination changes. Platform: |
Size: 2140160 |
Author:SALEH |
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Description: 本文分别实现了整体模板更新和选择性子模块更新方法，以适应运动目标的运动姿态变化以及运动背景变化，并将其分别与粒子滤波目标跟踪算法相结合，以提高跟踪的鲁棒性。-This thesis studies and implements a total target model updating method and a selected sub-model updating method, and then combines it with the particle filter algorithm for tracking object to improve the robustness of tracking. Platform: |
Size: 4749312 |
Author:孙杰 |
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Search - particle filter for object tracking