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针对粒子滤波方法在重采样阶段容易造成样本有效性和多样性的损失,导致了样本贫化问题,提出了一种改进的粒子滤波算法.算法将粒子群优化思想引入粒子滤波中,在粒子采样过程前先利用粒子群算法进行优化.粒子群算法将最新观测值融合到粒子进化公式中,大部分粒子经过粒子群优化后,朝着后验概率分布比较密集的区域运动,聚集在最优粒子附近,使粒子的权值被提高,避免了在重新采样过程中被舍弃,进而缓解了样本被贫化问题.目标跟踪系统中的位置估计由于物体运动具有突然性,很难准确估计.采用非线性目标跟踪模型和分时恒定值模型分别研究改进粒子滤波算法对误差均方值的影响.仿真结果表明改进算法与常规粒子滤波算法和扩展卡曼滤波算法相比,更加有效地降低变量的误差均方值,从而提高了滤波性能. 相似文献
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提出一种粒子滤波方法中粒子样本采样方法,旨在用较少的粒子数描述高维状态变量的后验概率分布.首先,研究特定人机交互条件下操作者的认知心理特点和人手运动特点,在此基础上提出了状态变量微观结构的基本概念;然后,进一步探讨描述微观结构的一般方法;最后,提出了基于状态变量微观结构的粒子采样方法和改进的粒子滤波跟踪算法.状态变量的微观结构为采样算法的设计提供了一种统一、高效的数学模型,以此为基础的采样算法可以有效避免对质量比较差的粒子样本进行大量采样.为了验证算法的有效性和性能,进行了大量实验,结果表明,与传统的粒子滤波方法相比,采用少量的粒子样本就可以达到较高的跟踪精度. 相似文献
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基于序列蒙特卡罗方法的3D人体运动跟踪 总被引:12,自引:2,他引:10
针对人体运动跟踪的特点,在退火粒子滤波方法的基础上,提出基于序列蒙特卡罗方法的3D人体跟踪算法.通过状态空间分解提高了退火系数选择的鲁棒性;同时,在每次退火时采用PERM采样方法,而不是标准的重采样,能在一定程度上抑制观测模型与真实分布之间的误差,从而提高算法的稳定性.通过模拟实验表明,该算法适合3D多关节人体跟踪. 相似文献
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樊玲 《计算机工程与应用》2011,47(23):121-123
针对低信噪比条件下微弱目标的检测和跟踪,提出了一种高斯粒子滤波检测前跟踪(TBD)方法。该方法采用高斯粒子滤波递归地估计目标的状态,结合固定样本长度(FSS)似然比检验实现了对微弱目标的检测和跟踪。由于避免了粒子滤波TBD方法中的重采样过程,高斯粒子滤波TBD方法没有采样枯竭现象,算法复杂度小。仿真实验表明,该算法对微弱目标具有良好的实时检测和跟踪性能。 相似文献
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粒子滤波算法由于其处理非线性非高斯的能力优势,目前应用领域非常广泛。然而粒子滤波中存在的粒子退化、样贫等问题同样不容忽视,针对这些问题提出了一种改进的重采样粒子滤波算法。该方法借鉴了部分分层重采样和残差重采样的思路,通过对粒子权值大中小分类,在兼顾粒子多样性的情况下用不同策略分层次复制三个集合样本,从而优化了重采样算法。最后通过与经典粒子滤波重采样算法和其他部分重采样(PR)算法相比,以一维非线性跟踪模(UNG)和二维纯角度跟踪模型(BOT)两个模型的仿真结果验证了所提算法的滤波性能和有效性。 相似文献
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基于视频的三维人体运动跟踪系统的设计与实现 总被引:2,自引:0,他引:2
在优化粒子滤波跟踪框架下,设计并实现了一个结合多种图像特征、在多摄像机环境下跟踪人体运动的三维人体运动跟踪系统.通过定义三维人体模型、摄像机模型以及观测似然模型,得到跟踪所需目标函数,并使用优化粒子滤波算法进行求解.实验结果表明,该系统能够对人体运动进行准确的跟踪和三维重建,可应用于体育运动分析和动画制作等领域. 相似文献
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尽管随机采样降低了陷入局部极值的风险,但不能保证收敛到全局最优.为此提出了一个将人体部件分割算法嵌入到粒子滤波框架的人体运动跟踪系统.首先使用Condensation算法传播并评估粒子,然后利用基于期望最大化的部件分割算法迭代更新粒子.在迭代过程中,从采样粒子推导的姿态用于部件分割,分割结果用于确定粒子分布,使粒子逐渐接近高似然区域,从而提高找到全局最优的概率并降低采样粒子数.在HumanEva-Ⅱ数据库上的测试结果表明了文中系统的有效性,且对比实验结果也优于Condensation算法和退火粒子滤波. 相似文献
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Pose estimation and tracking of an articulated structure based on data from multiple cameras has seen numerous applications in recent years. In this paper, a marker-based human pose tracking algorithm from multi view video sequences is proposed. The purpose of the proposed algorithm is to present a low cost motion capture system that can be used as an alternative to high cost available commercial human motion capture systems. The problem is defined as the optimization of 45 parameters which define body pose model and is solved using a modified version of particle swarm optimization (PSO) algorithm. The objective of this optimization is to maximize a fitness function which formulates how much the body model matches with 2D marker coordinates in video frames. A sampling covariance matrix is used in the first part of the velocity equation of PSO and is annealed with iterations. The sampling covariance matrix is computed adaptively, based on variance of parameters in the swarm. One of the concerns in this algorithm is the high number of parameters to define the model of body pose. To tackle this problem, we partition the optimization state space into six stages that exploit the hierarchical structure of the skeletal model. The first stage optimizes the six parameters that define the global orientation and position of the body. Other stages relate to optimization of right and left hand, right and left leg and head orientation. In the proposed partitioning method previously optimized parameters are allowed some variation in each step that is called soft partitioning. Experimental results on Pose Estimation and Action Recognition (PEAR) database indicate that the proposed algorithm achieves lower estimation error in tracking human motion compared with Annealed Particle Filter (APF) and Parametric Annealing (PA) methods. 相似文献
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Chenguang Liu Peng Liu Jiafeng Liu Jianhua Huang Xianglong Tang 《Journal of Intelligent and Robotic Systems》2010,58(2):109-124
In this paper, we develop a two-dimensional articulated body tracking algorithm based on the particle filtering method using
partitioned sampling and model constraints. Particle filtering has been proven to be an effective approach in the object tracking
field, especially when dealing with single-object tracking. However, when applying it to human body tracking, we have to face
a “particle-explosion” problem. We then introduce partitioned sampling, applied to a new articulated human body model, to
solve this problem. Furthermore, we develop a propagating method originated from belief propagation (BP), which enables a
set of particles to carry several constraints. The proposed algorithm is then applied to tracking articulated body motion
in several testing scenarios. The experimental results indicate that the proposed algorithm is effective and reliable for
2D articulated pose tracking. 相似文献
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针对粒子滤波目标跟踪算法粒子退化及跟踪精度问题,提出了一种基于马尔可夫链-蒙特卡罗(MCMC,Markov Chain Monte Carlo)的迭代平方根容积粒子滤波(ISRCPF,iterated square root cubature Kalman particle filter)算法(ISRCPF-MCMC).在该滤波算法中,利用容积数值积分原则计算非线性随机函数的均值和方差,通过正交矩阵分解代替矩阵开方,在生成的粒子滤波建议分布中融入当前量测值,提高对系统后验概率的逼近程度.然后在此基础上融合MCMC抽样算法(MH,Metropolis Hasting)对所选建议分布进行优化,增加粒子多样性,以提高跟踪精度.仿真试验结果表明,ISRCPF-MCMC算法的估计误差与其他算法相比降低至0.403%. 相似文献
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Zhimin Chen Yuanxin Qu Zhengdong Xi Yuming Bo Bing Liu Deyong Kang 《Asian journal of control》2016,18(5):1877-1890
The interacting multiple model based on a particle filter fails to meet the requirements of real‐time performance when manoeuvring target tracking by radar due to deficiencies in its high calculation complexity. An improved particle filter based on landscape adaptive particle swarm optimization is proposed. This filter adopts the method of updating inertia weight, using not only local information and global information, but also preventing algorithm trapping in a local optimum, so the filter can find the optimal solution with less iteration. Additionally, an improved tracking model is presented. With the help of systematic resampling, the model can figure out the model index of particles. The experimental results prove that the new tracking algorithm not only improves manoeuvring target tracking accuracy, but also decreases computing complexity. 相似文献
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针对粒子滤波跟踪算法计算代价大以及Meanshift跟踪算法容易陷入局部极值等问题,提出一种嵌入均值优化的粒子滤波跟踪算法。该算法根据粒子滤波的运动模型估计目标区域位置,利用Bhattacharyya距离度量粒子区域和目标模型的相似性,并根据相似性来更新粒子权值,使用Meanshift优化算法改善粒子的估计位置,使得这些粒子的候选区域能更加接近目标模板,极大提高了粒子的使用效率。实验结果表明,该算法能够有效进行人的跟踪,处理人的短暂遮挡问题,性能优于粒子滤波算法,有较好的实用性。 相似文献