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基于统计决策规则提出自适应采样数粒子滤波算法, 在定义综合性能风险函数的基础, 推导出粒子数与滤波误差方差之间的关系式, 使得在跟踪过程中, 可以根据目标的机动情况在线调节粒子数, 以使跟踪性能
达到最优。在Matlab仿真平台下进行了闪烁噪声下的机动目标跟踪实验, 结果表明, 自适应采样数粒子滤波算法是一种有效的机动目标跟踪方法, 跟踪性能较基本粒子滤波算法提高了3.17倍。-Based on statistical decision rules of the number of adap
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a real-time 3D pointing gesture
recognition algorithm for natural human-robot interaction
(HRI). The recognition errors in previous pointing gesture
recognition algorithms are mainly caused by the low performance
of the hands tracking module
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real-time 3D pointing gesture
recognition algorithm for natural human-robot interaction
(HRI). The recognition errors in previous pointing gesture
recognition algorithms are mainly caused by the low performance
of the hands tracking module an
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the Kernel Particle Filter
(KPF)—is proposed for visual tracking in image sequences.
The KPF invokes kernels to form a continuous estimate of the
posterior density function. Particles are allocated based on the
gradient information estimated
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本文分别就纯方位角跟踪中的机动目标跟踪、有信号传输时延时的跟踪及一类特定的多目标跟踪问题进行了较为系统和深入的研究。首先，针对非机动目标提出一种智能距离参数化无味滤波方法，与传统方法相比，改进了跟踪初始性能、滤波精度以及优化了系统资源。其次，针对机动目标纯方位角跟踪提出一种将交互多模型和核粒子滤波结合的方法，在维持跟踪精度的前提下，大幅减少了所需粒子数，改善了系统实时性。-This paper bearings-only tracking of maneuvering target tracki
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提出了一种基于粒子滤波的自适应运动目标跟踪方法。均值漂移算法是一种最优梯度下降法，通过
迭代来搜索目标，从而实现对运动目标的跟踪。-Adaptive moving target tracking method based on particle filter. The mean-shift algorithm is an optimal gradient descent method, through an iterative search for the target in order t
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针对遮挡、光照变化、尺度变化等复杂环境中的视觉跟踪问题, 提出一种基于后验概率
度量的粒子滤波跟踪算法-For shelter, illumination changes, scale variations in complex environments such as visual tracking problem, a posterior probability metrics based on particle filter tracking algorithm
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Particle filters are often used for tracking objects within a
scene. As the prediction model of a particle filter is often
implemented using basic movement predictions such as ran-
domwalk,constantvelocityoracceleration,thesemodelswill
us
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重点讨论了改进后的粒子滤波理论在复杂场景下运动物体跟踪问题。-It discusses the theoretical particle filter improved tracking of moving objects in a complex scene.
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This paper introduces a new object tracking method
which combines two algorithms working in parallel, and based on
low-level observations (colour and gradient orientation): the Generalised Hough Transform, using a pixel-based descr iption, and
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关于粒子滤波算法在目标跟踪时的学习文章，涵盖原理及算法实现-Particle filter algorithm in the target tracking learning articles, covering the principle and algorithm
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