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rob_hess_pf.粒子滤波现在已经成为目标跟踪领域的主流算法
- 粒子滤波现在已经成为目标跟踪领域的主流算法，它的应用范围广泛，在非线性、非高斯噪声下依然表现良好。该代码是Rob Hess 编写的。他的个人主页是：http://web.engr.oregonstate.edu/~hess/ ,Now,Partical filter has become the main algorithm in moving target tracking region.It still perform very well in nonlinear non-gaussia
cvfilter.rar
- 这个个程序是利用卡尔曼滤波实现人体关节点的跟踪问题，运用的是CV模型，噪声为高斯白噪声！有用的拿去看看！,This procedure is to use a Kalman filter to achieve a key point in human tracking problems, the use of the CV model, noise is Gaussian white noise! Take useful to see!
Monitor.rar
- 自己写的vc 读取YUV视频信号 进行帧差法进行目标跟踪 ，高斯背景模型提取，,Vc wrote it myself to read YUV video signal frame-difference method for target tracking, Gaussian background model extraction,
Gauss
- 运动跟踪Viual c++ 高斯背景建模-Motion Tracking Viual c++ Gaussian Background Modeling
1-4YCC
- 本代码是在VC平台实现的摄像头输入的目标跟踪，以高斯建模做的背景，希望对大家有帮助-The code is in the VC camera platform target tracking input to Gaussian background modeling done, I hope all of you help
LPFleida
- Pf粒子滤波实现的目标跟踪程序，可实现针对非高斯噪声情况下的跟踪-Pf particle filter to achieve tracking procedures, can be non-Gaussian noise for tracking cases
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.
EdgeContour
- 边缘检测,轮廓检测,Sebel算子,高斯拉普拉斯算子,Robert算子,Prewitt算子,Kersch算子等,Hough变换,平行线检测,轮廓提取,种子填充,轮廓跟踪-Edge detection, contour detection, Sebel operator, Gaussian Laplacian, Robert operator, Prewitt operator, Kersch operator, etc., Hough transform, parallel line detec
CostReference
- 一篇关于代价参考粒子滤波算法的论文，该算法的优点是不需要任何先验概率知识的假定和重采样过程，可实现并行处理。本文将代价参考粒子滤波与当前统计模型的优点相结合 ,提出一种新的当前统计模型自适应跟踪算法 ,用于非线性非高斯系统的机动目标跟踪。-A particle filter on the reference price of the paper, the advantages of the algorithm does not require any prior knowledge of the
496876399457457454534
- 粒子滤波技术在非线性、非高斯系统表现出来的优越性，决定了它的应用范围非常广泛。另外，粒子滤波器的多模态处理能力，也是它应用广泛有原因之一。国际上，粒子滤波已被应用于各个领域。在经济学领域，它被应用在经济数据预测；在军事领域已经被应用于雷达跟踪空中飞行物，空对空、空对地的被动式跟踪；在交通管制领域它被应用在对车或人视频监控；它还用于机器人的全局定位。 -Particle filter technology in the non-linear, non-Gaussian system demon
kalman
- kalman滤波器收到好的结果基于c++和opencv-Tracking of rotating point. Rotation speed is constant. Both state and measurements vectors are 1D (a point angle), Measurement is the real point angle+ gaussian noise. The real and the estimated points
LZongzheng
- 用于实现目标跟踪的MATLAB程序，针对非线性系统和高斯噪声-MATLAB is used to achieve target tracking procedure for nonlinear system and Gaussian noise
ADifferentiallyCoherentDelay-LockedLoopforSpread-S
- 详细讲述了直接序列扩频差分锁相环的文章，包含具体的算法结构，并附有仿真结果。-A novel differentially coherent delay-locked loop(DCDLL) for accurate code tracking is proposed for direct sequence spread spectrum systems. Due to the use of the differential decoder and exactly one correlato
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
GaussHermite
- 高斯hermit粒子滤波器，及其测试程序。可以用于目标跟踪。-Gaussian hermit particle filter, and testing procedures. Can be used for target tracking.
An-Improved-Mean-Shift-Algorithm
- 奉文主要针对经典的Mean Shift跟踪算法均匀剖分整个颜色空间造成许多空的直方图区间以及不能准确表达目标 颜色分布的缺点，提出J，一种改进算法．该改进算法首先对目标的颜色进行聚类分析，根据聚类结果通过矩阵分解和正交变换 自适席地剖分日标的颜色空间从向确定对戍于每一聚类的子空间．在此基础上定义 一种新的颜色模型，该模型统计落入每 一颜色子空间的像素的加权个数并用高斯分布建模每一个子空间的颜色分布，并推导r一种相似性度量米比较目标和候选目 标的颜色模型之间的相似程度．最后基于该颜
Gauss
- 两篇关于目标检测、跟踪的硕士论文，里面有关于多高斯背景模型的描述，作为学习，不错的资源-Two on target detection, tracking, master' s thesis, which has more than Gaussian background model for the descr iption, as a learning, a good resource
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
modulePparticlePfilter
- 这是用于目标跟踪的粒子滤波代码， 用matlab编写的，很有借鉴性，一维情况下， 非高斯非线性，其中将扩展卡尔曼滤波与粒子滤波进行比较，更好的说明了粒子滤波的优越性-This is a particle filter for target tracking code using matlab, referential nature, one-dimensional case, the non-Gaussian non-linear, which will be extended Kalm
435234paper
- 从边检测边跟踪的角度探讨了图象序列中机动目标的形心跟踪问题,深入分析了强高斯噪声背景下目标形心估计的统计性质及用于形心估计的图象预处理方法。-Explored from the perspective of the side edge-detection tracking the centroid tracking of maneuvering targets in the image sequence, in-depth analysis of the statistical properti