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Description: A new approach toward target representation and localization, the central component in visual tracking
of non-rigid objects, is proposed. The feature histogram based target representations are regularized
by spatial masking with an isotropic kernel. The masking induces spatially-smooth similarity functions
suitable for gradient-based optimization, hence, the target localization problem can be formulated using
the basin of attraction of the local maxima. We employ a metric derived from the Bhattacharyya
coefficient as similarity measure, and use the mean shift procedure to perform the optimization. In the
presented tracking examples the new method successfully coped with camera motion, partial occlusions,
clutter, and target scale variations. Integration with motion filters and data association techniques is also
discussed. We describe only few of the potential applications: exploitation of background information,
Kalman tracking using motion models, and face tracking.-A new approach toward target representation and localization, the central component in visual trackingof non-rigid objects, is proposed. The feature histogram based target representations are regularizedby spatial masking with an isotropic kernel. The masking induces spatially-smooth similarity functionssuitable for gradient-based optimization, hence, the target localization problem can be formulated usingthe basin of attraction of the local maxima. We employ a metric derived from the Bhattacharyyacoefficient as similarity measure, and use the mean shift procedure to perform the optimization. In thepresented tracking examples the new method successfully coped with camera motion, partial occlusions, clutter, and target scale variations. Integration with motion filters and data association techniques is alsodiscussed. We describe only few of the potential applications: exploitation of background information, Kalman tracking using motion models, and face tracking .
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Description: documentation for optimal filtering toolbox for mathematical software
package Matlab. The methods in the toolbox include Kalman filter, extended Kalman filter
and unscented Kalman filter for discrete time state space models. Also included in the toolbox
are the Rauch-Tung-Striebel and Forward-Backward smoother counter-parts for each filter, which
can be used to smooth the previous state estimates, after obtaining new measurements. The usage
and function of each method are illustrated with five demonstrations problems.
1
-documentation for optimal filtering toolbox for mathematical softwarepackage Matlab. The methods in the toolbox include Kalman filter, extended Kalman filterand unscented Kalman filter for discrete time state space models. Als
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Author: eestarliu |
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Description: documentation for optimal filtering toolbox for mathematical software
package Matlab. The methods in the toolbox include Kalman filter, extended Kalman filter
and unscented Kalman filter for discrete time state space models. Also included in the toolbox
are the Rauch-Tung-Striebel and Forward-Backward smoother counter-parts for each filter, which
can be used to smooth the previous state estimates, after obtaining new measurements. The usage
and function of each method are illustrated with five demonstrations problems.
1-documentation for optimal filtering toolbox for mathematical softwarepackage Matlab. The methods in the toolbox include Kalman filter, extended Kalman filterand unscented Kalman filter for discrete time state space models. Als
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Size: 640000 |
Author: eestarliu |
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Description: 自编卡尔曼递推程序，估计信号x(t)的波形。x(t)是一连续平稳的随机信号，自相关函数已知，信号x(t)为加性噪声所干扰，测量值的离散值z(k)也已知。-Recursive Kalman own procedures, it is estimated that the signal x (t) waveform. x (t) is a smooth continuous random signals, autocorrelation function is known, the signal x (t) for the additive noise disturbance to the measured value of the discrete value of z (k) is also known.
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Author: 王健 |
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Description: A new approach toward target representation and localization, the central component in visual tracking of nonrigid objects,
is proposed. The feature histogram-based target representations are regularized by spatial masking with an isotropic kernel. The
masking induces spatially-smooth similarity functions suitable for gradient-based optimization, hence, the target localization problem
can be formulated using the basin of attraction of the local maxima. We employ a metric derived from the Bhattacharyya coefficient as
similarity measure, and use the mean shift procedure to perform the optimization. In the presented tracking examples, the new method
successfully coped with camera motion, partial occlusions, clutter, and target scale variations. Integration with motion filters and data
association techniques is also discussed. We describe only a few of the potential applications: exploitation of background information,
Kalman tracking using motion models, and face tracking.
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Size: 2459648 |
Author: Ali |
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Description: 11.1 傅里叶级数逼近kfour.c
11.2 快速傅里叶变换kkfft.c
11.3 快速活什变换kkfwt.c
11.4 五点三次平滑kkspt.c
11.5 离散随机线性系统的卡尔曼滤波klman.c
11.6 af-bt-gm滤波kkabg.c-11.1 Fourier series approximation kfour.c 11.2 Fast Fourier Transform kkfft.c 11.3 fast living and even transform kkfwt.c 11.4 5.3 times smooth kkspt.c 11.5 Discrete Stochastic Linear Systems with Kalman Filter klman.c 11.6 af-bt-gm filter kkabg.c
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Size: 9216 |
Author: yangasdtat |
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Description: 利用vc++编写的傅里叶级数逼近、快速傅里叶变换、快速沃什变换、快速三次平滑、离散随机系统的卡尔曼滤波、α-β-γ滤波，希望有帮助-Using vc++ written in Fourier series approximation, fast Fourier transform, fast Walsh transform, fast three times a smooth, discrete-time stochastic system, Kalman filter, α-β-γ filter, want to help
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Author: 张林 |
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Description: 卡尔曼滤波器，有卡尔曼初级教学，平滑卡尔曼滤波，升级卡尔曼滤波，航迹追踪等功能-KALMAN FILTER, which includes the basic learing of Kalman, the smooth of Kalman,the Update of Kalman and so on
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Author: 周挺 |
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Description: Simulate GPS tracking Object 2D and smooth its obits by Kalman filter.
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Size: 1024 |
Author: kamdulong |
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Description: 假设用一观测器从t=1秒开始对一个运动目标的距离进行连续地跟踪测量，假设观测的间隔为一秒钟，雷达到运动目标之间的距离为 常数
分析上述对象，建立系统模型，构造卡尔曼滤波器，编程计算，求：
距离S(t-5)的最佳平滑及估计误差，
-Suppose observer with a t = 1 seconds starting from a continuously moving target tracking distance measurement, assuming a second interval of observation, radar, the distance between the moving target is a constant analysis of the object, create a system model, Kalman filter structure, programming, computing, requirements: from the S (t-5) and estimate the optimal smoothing error,
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Size: 1024 |
Author: 马存尊 |
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Description: 图像进行运动估计时，可用此平滑曲线，求出抖动分量，从而可以进行运动补偿-Image motion estimation, can use this smooth curve, seek the jitter component, and can exercise compensation
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Size: 1024 |
Author: huangjiulin |
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Description: A new approach toward target representation and localization, the central component in visual tracking of nonrigid objects,
is proposed. The feature histogram-based target representations are regularized by spatial masking with an isotropic kernel. The
masking induces spatially-smooth similarity functions suitable for gradient-based optimization, hence, the target localization problem
can be formulated using the basin of attraction of the local maxima. We employ a metric derived from the Bhattacharyya coefficient as
similarity measure, and use the mean shift procedure to perform the optimization. In the presented tracking examples, the new method
successfully coped with camera motion, partial occlusions, clutter, and target scale variations. Integration with motion filters and data
association techniques is also discussed. We describe only a few of the potential applications: exploitation of background information,
Kalman tracking using motion models, and face tracking.
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Size: 2439168 |
Author: Felix |
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Description: 蜂窝网定位中基于chan算法的TDOA定位算法，并将定位结果用卡尔曼滤波器平滑-an algorithm of TDOA location based on Chan for celular location ,kalman filter is used to smooth the data
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Size: 3072 |
Author: 李一 |
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Description: 发散的一个新的Rauch-Tung-Striebel形式容积卡尔曼平滑了非线性状态空间模型采用求容积法为最优平滑转换-A new form of Rauch-Tung-Striebel volume divergence Kalman smoother nonlinear state space model using the volumetric method for seeking the optimal smooth transition
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Size: 1433600 |
Author: mm |
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Description: 关于导航定位中的卡尔曼滤波程序。X1，X2分别代表经过转换以后的位置X,Y轴的坐标。V1，V2为其相对应的速度。卡尔曼滤波可以使定位数据更加平滑。-Calman filtering program in navigation and positioning. X1, X2, respectively, after conversion to the position of the X, Y axis coordinates. V2, V1 for its relative speed. Calman filter can make the positioning data more smooth.
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Size: 32768 |
Author: 阿飞 |
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Description: 智能平衡小车程序 卡尔曼滤波 pid程序 mpu6050读取角度 可以平稳站立测试通过-Intelligent balance car program Kalman filter pid program mpu6050 angle can be read by a smooth test stand
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Author: 政 |
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Description: 卡尔曼滤波示例程序，采用matlab生成一个随机数组，采用卡尔曼滤波算法把随机数组进行平滑处理-Kalman filter sample program using matlab to generate a random array, using the Kalman filter algorithm to smooth the random array
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Size: 8192 |
Author: 张志豪 |
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Description: On the basis of Kalman filter, the raw data signal can be converted to a more smooth signal.
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Size: 1024 |
Author: Benjamin |
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Description: 使用卡尔曼算法对输入的数据进行平滑滤波，减小噪声的影响，可适用于语音增强，运动轨迹平滑等(The Kalman algorithm is used to smooth the input data and reduce the influence of noise. It can be applied to speech enhancement, motion trajectory smoothing and so on.)
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Author: billwangyong |
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