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高斯背景建模分析
- 高斯背景建模分析，包括前景，背景提取
BGM
- 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 Kalman滤波为运动模型实现对运动目标的连续跟踪。在目标跟踪时，该算法针对目标遮挡引起的各种可能情况.-In this paper, a stationary camera under the conditions of the moving target detection and tr
mybss
- 盲信号分离是当前信号处理研究的热点课题之一，在无线数据通信、医学、语音以及地震信号处理等领域有着广阔的应用前景。基于负熵最大的FastICA算法用于实现盲信号分离。该方法的基本思路是以非高斯信号为研究对象，在独立性假设的前提下，对多路观测信号进行盲源分离。在满足一定的条件下，能够从多路观测信号中，较好地分离出隐含的独立源信号。-Blind signal separation is the study of signal processi
115157707GSM_detector
- 高斯模型GMM分割背景和前景高斯模型GMM分割背景和前景-GMM Gaussian model background and the prospect of partition GMM Gaussian model background and the prospect of partition
BackgroundSubtraction
- 减背景算法，基于背景建模的方法获取前景目标，采用高斯混合模型-By the background algorithm, based on background modeling method to get the prospect of goals, the use of Gaussian mixture model
Gauss
- 高斯背景建模，利用opencv进行处理，并且提取出前景-Gaussian background modeling, to deal with the use opencv, and the prospect of extracting
GMM
- 利用K-高斯混合模型提取视频的前景信息。-The use of K-Gaussian mixture model for the future of video information extraction
Background_GMM
- 混合高斯模型，建立背景模型，从而可以分离前景与背景-Gaussian mixture model, background model, which can be separated from foreground and background
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 publ
Tracking_Cars_Using_Singal_Gaussian
- 通过单高斯来建立背景，然后用背景减法来提取情景，并对前景进行跟踪和计数。-We establis the background through the method of singal Gaussian,then use the background substration to get foreground,we are also successed to get the counts of cars and to track the
xiaobo
- ．首先，建 立背景的混合高斯分布模型和阴影颜色模型，通过差分法提取前景区域并结合Gabor小 波纹理特征分析找出潜在的阴影点；然后通过阴影颜色模型对这些潜在的阴影点进行颜 色分析；最后通过后续处理，找出真正的阴影区域-. First of all, to establish the background Gaussian mixture distribution model and the shadow color mode
GMM
- 利用OPENCV來實現高斯混合模型的背景相減，可看到當前影像、前景及背景-OPENCV to achieve using GMM background subtraction, we can see the current image, foreground and background
mixGuass
- 利用混合高斯模型进行前景提取，能够达到较好的检测结果-mix Guass， objects extracting
GMM3
- 基于混合高斯模型的运动目标检测，能实时检测出完整运动前景，是本人对原来的高斯模型的改进-Gaussian mixture model based motion detection, real-time full motion detection prospects are my original Gaussian model improvements
demo
- 利用训练集训练一个高斯模型，进行运动目标的提取（文件中包含数据集）(Use the training set to train a Gaussian model to extract the moving object (the file contains the data set))
背景差GMM
- opencv，vs2010 利用混合高斯模型，得到运动前景，与静态背景(Opencv and VS2010 use hybrid Gauss model to obtain motion foreground and static background)
21
- 21.【高斯处理视频并跟踪运动做前景背景分割】bgfg2(21. [Gauss processing video and tracking movement, foreground background segmentation] bgfg2)
混合高斯
- 用于车辆检测背景建模 通过混合高斯将前景与北京分离(Vehicle tracking background modeling is used to extract foreground)
MATLAB_高斯模型
- 用高斯模型算法来处理视频，提取前景信息，适合动态背景(Gauss model algorithm is used to process video and extract foreground information, which is suitable for dynamic background)
改进的高斯混合背景模型的实现
- 利用改进的高斯混合模型对前景目标的提取有较好的作用，这是基于OpenCV的C++程序，请安装OpenCV库进行调试(The improved Gauss mixture model has a good effect on foreground target extraction. This is a C++ program based on OpenCV. Please install OpenCV library for debug