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GMM
- 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 objec
bodymotiondetection
- 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.
GMM_RGB
- 基于混合高斯的运动目标检测的跟踪！修改后可以使用。-Gaussian mixture-based tracking of moving target detection! Modifications can be used.
Vehicle_Tracker_with_background_subtraction_and_k
- 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
matlab_v
- 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 sequentiall
cvbgfg_gaussmix
- 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 mix
GMM
- 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
SIFT_VC.lib
- 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
motion3
- 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 backgr
TrackingBlobAlgorithms
- 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 detec
GaussDetect
- 基于高斯混合运动背景模型的运动目标检测和跟踪源程序-Moving target detection and tracking the source of the background model based on Gaussian mixture motion
GM-PHDsmooth
- 检测前跟踪 粒子滤波 概率假设密度 高斯混合粒子 平滑-Pre-test tracking particle filter probability hypothesis density Gaussian mixture particle smoothing
Adaptive-background-mixture-models
- 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.
KaewTraKulPong
- opencv中混合高斯模型实现方法的参考文献-An Improved Adaptive Background Mixture Model for Real-time Tracking and Shadow Detection
GMM_OpenCV
- 用opencv编写的GMM，用于进行前景检测，人物探测，目标跟踪-Gaussian Mixture Model for human tracking
gausstrack
- 利用混合高斯模型进行运动跟踪的源代码，vc++和opencv，需要有摄像头支持。-The use of Gaussian mixture model motion tracking the source code vc++ and opencv, camera support.
5
- 了适应跟踪过程中目标光照条件的变化，并对目标特征进行在线更新，提出一种将局部二元模式(LBP) 特征与图像灰度信息相融合，同时结合增量线性判别分析对目标进行跟踪的算法．跟踪开始前，为了获得比较准确的目标描述，使用混合高斯模型和期望最大化算法对目标进行分割；跟踪过程中，通过蒙特卡罗方法对目标区域和背景区域进行采样，并更新特征空间参数．得到目标和背景的最优分类面；最后使用粒子滤波器结合最优分类面对目标状态进行预测．通过光照变化的仿真视频和自然场景视频的跟踪实验，验证了文中算法的有效性．-Trac
real-time-tracking
- 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
meanshift-tracking
- 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
GM-PHD1
- 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 detectio