该方法利用背景差分法与粒子滤波器算法相结合。
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。
Then an algorithm based on disparity background difference was employed to detect the foreground region.
然后，介绍了基于粒子滤波器的移动机器人定位研究进展。
Secondly, the progress of mobile robot localization based on particle filters is described.
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。
To overcome the limitation of the traditional tracking algorithm, we use particle filter for face tracking.
应用粒子滤波器及其改进算法解决了导航系统中存在的强非线性问题。
The strong nonlinearity of navigation system is solved using the particle filtering and its improved algorithm.
以粒子滤波器估计的目标位置作为先验知识，可以改进前景目标位置的重构。
Taking a prior knowledge as target position which is estimated by a particle filter, it is possible to improve the reconstruction of foreground target.
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。
The proposed algorithm is a combination of the partition sampling technique and hypothesis calculations with the particle filter.
该文研究了杂波干扰下适用于平面机动目标实时定位的粒子滤波器的设计和实现。
This paper studies the design and implementation method of particle filters suitable for the plane maneuvering targets real time tracking.
将集群智能思想引入粒子滤波，提出一种新颖的基于人工鱼群算法的粒子滤波器。
By bringing the thought of swarm intelligence into particle filtering, a novel particle filter based on the artificial fish school algorithm is proposed.
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。
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。
A new approach that incorporates robust appearance filter with a particle filter to realize robust object tracking is presented in this paper.
包含位置式PID算法、积分分离式pid，多目标跟踪的粒子滤波器，包括脚本文件和函数文件形式。
It contains positional PID algorithm, integral separate PID, Multi-target tracking particle filter, Including SCR ipt files and function files in the form.
介绍了作为粒子滤波理论基础的递推贝叶斯估计的基本概念，说明了重要性函数对于粒子滤波器的设计是至关重要的。
The principle of Recursive Bayesian estimation was introduced which was the basis of Particle filter, and the significance of importance function to the design of particle filter was illustrated.
提出了一种结合粒子滤波器和吉布斯采样器的多机动目标跟踪算法，可以很好地解决在杂波环境下的多机动目标跟踪问题。
So a new multitarget tracking algorithm is proposed, which combines the particle filter and the Gibbs sampler, and can track maneuvering multitarget in cluttered environment very.
将TVAR模型的信号和反射系数矢量增广为状态矢量后，应用高斯粒子滤波器(GPF)估计TVAR的模型参数，构造了语音增强算法。
When TVAR model signal and reflection coefficients were extended to state vector, Gaussian Particle Filter (GPF) was applied to estimate parameters of TVAR model.
仿真结果表明，对于纯方位跟踪问题，UPF不仅解决了扩展卡尔曼滤波器的线性化损失难题，而且与PF等粒子滤波器相比，具有更高的跟踪精度。
The results show that the UPF not only solves the linearized loss problem in the extended Kalman filter, but also is more accurate than the PF in the BOT.
仿真结果表明，对于机动目标被动跟踪问题，GSPF不仅具有较高的跟踪精度，而且与一般粒子滤波器相比，GSPF具有较好的跟踪稳定性和较低的计算量。
The results show that the GSPF not only has high tracking accuracy in passive tracking, but also has better stability and less computation amount than the PF.
本文提出并实现了一套完整的行人跟踪系统，整个系统的底层是由HOG特征和颜色直方图特征构成的行人检测器，上层则采用粒子滤波器算法，结合各个行人检测器的结果得到最终检测结果。
In this article, we proposed and implemented a human tracking system whose bottom layer is human detectors based HOG feature and color histogram feature, and upper layer is based on particle filter.
在网络综合法设计的滤波器电路基础上，利用粒子群优化算法对滤波器的整个参数空间进行高效并行搜索直到获得最优的参数值。
The filter is designed by the network synthesis design method, and it optimizes the circuit's parameters in the whole parameters space effectively and globally by PSO until gain the best parameters.
在网络综合法设计的滤波器电路基础上，利用粒子群优化算法对滤波器的整个参数空间进行高效并行搜索直到获得最优的参数值。
The filter is designed by the network synthesis design method, and it optimizes the circuit's parameters in the whole parameters space effectively and globally by PSO until gain the best parameters.
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