In this paper, we introduce the particle filter algorithm.
本文采用的是粒子滤波算法。
This algorithm combines background difference with the particle filter.
该方法利用背景差分法与粒子滤波器算法相结合。
This paper presents a particle filter-based algorithm for IR target-tracking.
提出一种基于粒子滤波的红外目标跟踪的新算法。
Mean shift and particle filter are their typical representatives, respectively.
均值偏移和粒子滤波是它们的典型代表。
Hybrid particle filter based flame tracking algorithm is proposed in this paper.
提出一种基于混合粒子滤波的运动火焰跟踪算法。
In chapter two, the application of particle filter in tracking target is investigated.
第二章介绍粒子滤波在目标跟踪中的应用。
Tracking method based on particle filter is the main tracking method in complex scenes.
基于粒子滤波的跟踪方法成为复杂场景目标跟踪的主流方法。
Particle filter (PF) is an optimal nonlinearity filtering method which rises in recent years.
粒子滤波（PF）是近年来兴起的一种最优非线性滤波方法。
As for the target's track prediction, this paper USES the particle filter tracking algorithm.
而对于目标运动轨迹的预测，本文采用粒子滤波跟踪算法。
To improve the performance of passive tracking, the Gaussian sum particle filter (GSPF) was proposed.
为提高被动跟踪性能，提出了一种高斯和粒子滤波方法。
To overcome the limitation of the traditional tracking algorithm, we use particle filter for face tracking.
为了克服传统跟踪算法的局限性，采用粒子滤波器进行人脸跟踪。
Compared with the particle filter (PF), it avoids the resampling step and the particle degeneracy phenomenon.
与粒子滤波算法相比，其优点是不需要重采样步骤和不存在粒子退化现象。
A new Gaussian particle filter (GPF) is discussed to solve estimation problems in nonlinear non-Gaussian systems.
为了解决非线性、非高斯系统估计问题，讨论了一种新的滤波方法——高斯粒子滤波算法。
My main work is applying the particle filter algorithm to random simulate the non-linear Bayesian dynamic models.
我的主要工作是把粒子滤波算法引人到非线性贝叶斯动态模型中来，对非线性模型进行了模拟。
Aiming at the choice of proposal function and degeneracy problem in particle filter, an improved algorithm is put forward.
针对粒子滤波算法建议性函数的选择问题和粒子匮乏现象，提出了改进粒子滤波算法。
As other predictive filters, state space is recursively got from measure space with system model by using the Particle filter.
这种滤波和其他预测性滤波一样，可以通过模型方程由测量空间递推得到状态空间。
The Particle Filter can resolve a problem on nonlinear and non-Gaussian model, and has been applied successfully in many fields.
它可以处理模型方程为非线性、噪声分布为非高斯分布的问题，在许多领域得到了成功的应用。
The proposed algorithm is a combination of the partition sampling technique and hypothesis calculations with the particle filter.
这种新的贝叶斯滤波算法是粒子滤波器与划分采样技术和假设计算的有机结合。
To track objects with scale changes in a complex scene, a particle filter based object tracking approach is proposed in this paper.
针对复杂情况下变尺度目标跟踪问题，提出一种基于粒子滤波的自适应尺度目标跟踪算法。
Particle filter algorithm has shown its good performance in non-linear and non-Gaussian models and is paid more and more attention.
粒子滤波算法由于其在非线性、非高斯模型中所表现出的优良性能，使得其越来越受到人们的重视。
When estimating the target state, particle filter is adopted, and the observation model is designed based on the adaptive appearance model.
在估计目标状态时，采用了粒子滤波算法，设计了基于自适应表面模型的观测模型；
The improved particle filter tracking algorithm not only keeps the high efficient operation, but also improves stability of target tracking.
改进后的粒子滤波跟踪算法不但保持了较高的运算效率，而且还较好地提高了跟踪的稳定性。
In tracking, the area of face contour distortion were searched by the method of particle filter, and the face contour parameters were solved.
同时，利用非线性滤波方法在人脸轮廓的变形区域内进行搜索，求解人脸轮廓参数，进行人脸轮廓跟踪。
A new approach that incorporates robust appearance filter with a particle filter to realize robust object tracking is presented in this paper.
本文提出一种新的目标跟踪算法，将鲁棒外观滤波与粒子滤波器相结合，从而实现稳定的目标跟踪。
Using UD decomposing to modify EKF Particle filter was imported into the navigation scheme based on the measurement of elevation Angle of star.
用UD分解改进EKF粒子滤波算法，并将其应用于基于星光仰角测量的探测器自主导航方案。
Furthermore, Bhattacharyya distance is employed to estimate the similarity between the target model and each hypotheses of the particle filter.
利用巴特查理亚距离描述粒子与目标颜色模型的相似性，作为粒子更新权值的有力依据。
In this paper, an adaptive particle filter for tracking application is proposed, which is based on tuning particle number and sampling interval.
本文提出了一种用于跟踪系统的可在线调整采样周期和粒子数目的自适应粒子滤波器。
Two optimal algorithms for particle filter algorithm are proposed in the paper: particle position and the interpolation-based tracking algorithms.
本文针对粒子滤波算法提出了两种改进算法：基于粒子位置优化和基于插值优化的粒子滤波跟踪算法。
Experimental results show that particle filter tracking algorithm is more stable and more accurate than traditional particle filter tracking algorithm.
试验表明，改进后的粒子滤波跟踪算法目标跟踪更加稳定，目标定位更加准确。
Experimental results show that particle filter tracking algorithm is more stable and more accurate than traditional particle filter tracking algorithm.
试验表明，改进后的粒子滤波跟踪算法目标跟踪更加稳定，目标定位更加准确。
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