Description: 高斯混合模型，及背景减计算，Blob连通域，统计流量计数-Gaussian mixture model, and background by calculation, Blob-connected domain, the number of statistical flowmeter Platform: |
Size: 5268480 |
Author:郜建军 |
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Description: 学位论文；运动物体跟踪方法主要包括卡尔曼滤波，Mean-shift，Camshifi算法，粒子滤波器，Snake模型等；应用卡尔曼滤波方法设计了一套煤矿矿工出入自动监测系统；提出了一种新的基于高斯混合模型的颜色特征提取方法，该方法克服了现有的Camshift算法Continuousl y Adaptive eanshift中跟踪目标特征提取精确度低和计算复杂度高的缺陷-Dissertation moving object tracking methods include Kalman filtering, Mean-shift, Camshifi algorithm, particle filter, Snake model the application of Kalman filtering method designed a coal miners out of automatic monitoring system a new Gaussian mixture model based on the color feature extraction method to overcome the existing Camshift algorithm Continuousl y Adaptive eanshift track target feature extraction accuracy and low computational complexity and high defects Platform: |
Size: 1343488 |
Author:田卉 |
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Description: Vehicle Tracking using a background subtraction based on mixture of Gaussians, and Kalman filtering to remove noise.
Require OpenCV to be installed.
By Jonathan Gagne
University of Waterloo
jgagne@uwaterloo.ca Platform: |
Size: 13237248 |
Author:jon |
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Description: Motion Tracking
=== === ===
This tarball contains all code required to run the tracking algorithm
on a sequence of images. Run the file run_tracker.m in Matlab and
follow the instructions. You will need to have a directory of
sequentially numbered images available. After entering the path and
file types the tracker will begin processing. Once the data window
appears the algorithm begins building a background model and attempts
to track objects. By clicking on any of the four subwindows you can
investigate the background representation (a Mixture of Gaussians) of
any pixel. The two windows that then appear display the mixture once
as a two-dimensional scatter plot (ignoring the blue colour
component), and once as a one-dimensional evolution of the red colour
component only. These plots make the internal processing visible and
should help determining suitable parameters to be set in
mixture_parameters.m.-Motion Tracking
===============
This tarball contains all code required to run the tracking algorithm
on a sequence of images. Run the file run_tracker.m in Matlab and
follow the instructions. You will need to have a directory of
sequentially numbered images available. After entering the path and
file types the tracker will begin processing. Once the data window
appears the algorithm begins building a background model and attempts
to track objects. By clicking on any of the four subwindows you can
investigate the background representation (a Mixture of Gaussians) of
any pixel. The two windows that then appear display the mixture once
as a two-dimensional scatter plot (ignoring the blue colour
component), and once as a one-dimensional evolution of the red colour
component only. These plots make the internal processing visible and
should help determining suitable parameters to be set in
mixture_parameters.m. Platform: |
Size: 36511744 |
Author:gobsy |
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Description: 利用混合高斯模型进行前景检测的源代码实现，依据的是Stauffer发表的Adapptive background mixture models for real-time tracking.-The prospects for the use of Gaussian mixture model, detection of the source code implementation, based on the Stauffer published Adapptive background mixture models for real-time tracking. Platform: |
Size: 5120 |
Author:connie |
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Description: 学习opencv图像处理中人体目标跟踪的一些很有用的资料，主要是讲camshift,meanshift和高斯混合模型。-Learning opencv image-processing for target tracking in the human body a number of very useful information, mainly speaking camshift, meanshift and Gaussian mixture model. Platform: |
Size: 1454080 |
Author:yuyu |
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Description: 三篇经典的背景建模文章，包括自适应混合高斯建模，无参的核密度建模等，对于运动检测与跟踪的研究生来说非常具有参考价值。-Background modeling three classic articles, including the adaptive Gaussian mixture modeling, non-participation of the nuclear density modeling, motion detection and tracking for graduate students who have great reference value. Platform: |
Size: 1213440 |
Author:he |
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Description: !本文提出了一种静止摄像机条件下的运动目标检测与跟踪算法,它以一种改进的自适应混合高
斯模型为背景更新方法-用连通区检测算法分割出前景目标-以./01/2滤波为运动模型实现对运动目标的
连续跟踪-! This paper, a stationary camera for moving target detection and tracking algorithm, based on a modified adaptive Gaussian mixture model for the background updating- with Components of the segment the foreground object detection algorithm- to ./01/2 filter for the motion model to achieve continuous tracking of the moving target Platform: |
Size: 495616 |
Author:xxx |
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Description: 混合高斯模型做的视频跟踪系统，具有良好的跟踪效果-Gaussian mixture model to do a video tracking system, has a good tracking results Platform: |
Size: 436224 |
Author:张学贺 |
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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: |
Size: 111616 |
Author: |
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Description: 本代码建立高斯混合模型(高斯多模型)(GMM)，将其用于计算机视觉领域的视频目标检测/视频监控/运动检测/运动目标检测/视频目标跟踪等相关应用中。-This code sets up Gaussian mixture model(Gaussion Mixture Model, GMM) with image sequences for the research and application field of computer vision, using GMM for visual object detection(video object detection), visual surveillance, motion detection, motion tracking, visual object tracking, video object tracking, etc. Platform: |
Size: 16384 |
Author:朱亮亮 |
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Description: 基于混合高斯模型的多目标跟踪算法，对背景图像建立混合高斯模型，实时更新高斯模型，达到更新背景的目的。-Gaussian mixture model-based multi-target tracking algorithm, Gaussian mixture model to establish the background image, real-time updates Gaussian model, to achieve the purpose of updating the background. Platform: |
Size: 586752 |
Author:廖家亮 |
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Description: Adaptive background mixture models for real-time tracking，做视频运动检测与跟踪的朋友可以看看这一篇经典论文。-Adaptive background mixture models for real-time tracking, to do the video motion detection and tracking of friends can look at a classic paper. Platform: |
Size: 526336 |
Author:YanZhi |
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Description: 自适应背景混合模型用于目标的实时追踪，介绍了目标追踪的新方法-Real-time adaptive background mixture model for tracking targets, introduced a new method of target tracking Platform: |
Size: 526336 |
Author:lihao |
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Description: 本算法实现的是目标的跟踪，采用的是混合高斯模型建立背景，然后用meanshift进行跟踪，包括使用MFC进行界面编辑-The algorithm is to track the target, using Gaussian mixture model background, and then meanshift track, including the use of interfacial MFC edit Platform: |
Size: 8729600 |
Author:zhangcheng |
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Description: Over-the-horizon radar (OTHR) exploits skywave propagation
of high-frequency signals to detect and track targets,
which are different from the conventional radar. It
has received wide attention because of its wide area surveillance,
long detection range, strong anti-stealth ability,
the capability of the long early warning time, and so on.
In OTHR, a significant problem is the effect of multipath
propagation, which causes multiple detections via
different propagation paths for a target with missed detections
and false alarms at the receiver [1–6]. Nevertheless,
the conventional tracking algorithms, such as
probabilistic data association (PDA) [7–9], presume that
a single-measurement per target, it may consider the
other measurements of the same target as clutter, and
multiple tracks are produced when a single target is
present. Therefore, these methods cannot effectively
solve the multipath propagation problem.(Conventional multitarget tracking systems presume that each target can produce at most one measurement
per scan. Due to the multiple ionospheric propagation paths in over-the-horizon radar (OTHR), this assumption is
not valid. To solve this problem, this paper proposes a novel tracking algorithm based on the theory of finite set
statistics (FISST) called the multipath probability hypothesis density (MP-PHD) filter in cluttered environments.
First, the FISST is used to derive the update equation, and then Gaussian mixture (GM) is introduced to derive
the closed-form solution of the MP-PHD filter. Moreover, the extended Kalman filter (EKF) is presented to deal
with the nonlinear problem of the measurement model in OTHR. Eventually, the simulation results are provided
to demonstrate the effectiveness of the proposed filter.) Platform: |
Size: 18432 |
Author:ioeyoyo |
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Search - mixture tracking