the attached file consists of matlab code for implementation of sequential importance sampling particle filter given in IEEE paper entitled as "A TUTORIAL ON PARTICLE FILTERS FOR ONLINE NONLINEAR NON GUASSIAN BAYESIAN TRACKING"

通过单一的Wi-Fi接入点的信号强度来判断移动物体的位置。比较新的一篇文章。用了蒙特卡罗抽样的办法－Monte Carlo Sampling Method－来估计位置。-This paper describes research towards a system for locating wireless nodes in a home environment requiring merely a single access point. The only sensor reading

“Fast Tracking via Dense Spatio-Temporal Context Learning,” In ECCV 2014的源代码，效果非常好。-In this paper, we present a simple yet fast and robust algorithm which exploits the spatio-temporal context for visual tracking. Our approach formulates the spatio-te

感知行为的影响因素包括单个关节的动作和不同关节的组态。因此提出一种新的基于关节的位置差异的特征类型，联合包括静态姿势、动作、位移在内的行为信息进行识别。采用关节在两个时间和空间区域的差异来明确地模拟个别关节动力学和不同关节的组态。然后应用主成分分析（PCA）来获得所需的特征。同时应用非参数的简捷的贝叶斯最近邻（NBNN）分类器进行多类行为的分类。这个NBNN分类器避免了帧描述符的量化，计算“图像到类别”的距离而不是“图像到图像”的距离。15到20帧的数据就足以实现手势以及动作的识别，无需应用整个