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Remarkable local resampling based on particle filter for visual tracking

机译：基于粒子滤波器的出色局部重采样，可进行视觉跟踪

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摘要

Generally, particle filters need a large number of particles to approximate the posterior for the purpose of ideal effect. Previous methods extract remarkable particles from the particles at time t-1 by nonlinear function. Those methods use the remarkable particles to reduce the number of particles and improve the accuracy of particle filter. However, the nonlinear function extracts the remarkable particles, which will weaken or even ignore useful remarkable local particles. Thus this paper presents a new resampling scheme to extract remarkable local particles. We propose a weight threshold and a distance threshold to extract remarkable local particles from particles at time t-1. Meanwhile, we use these remarkable local particles to track the target analytically. Besides, we propose a global transition model to improve the accuracy of the particle filter. Based on remarkable local resampling scheme and the global transition model, we propose a new framework of particle filter. Finally, experiments show that our framework has higher efficiency than previous methods in the case of fewer particles.
机译：通常，为了达到理想效果，粒子过滤器需要大量粒子来近似后验。先前的方法通过非线性函数从时间t-1处的粒子中提取出显着粒子。这些方法使用非凡的颗粒来减少颗粒数量并提高颗粒过滤器的精度。但是，非线性函数会提取显着粒子，这将削弱甚至忽略有用的显着局部粒子。因此，本文提出了一种新的重采样方案以提取显着的局部粒子。我们提出了权重阈值和距离阈值，以从时间t-1的粒子中提取出显着的局部粒子。同时，我们使用这些引人注目的局部粒子来分析目标。此外，我们提出了一个全局过渡模型来提高粒子滤波器的精度。基于出色的局部重采样方案和全局转换模型，我们提出了一种新的粒子滤波框架。最后，实验表明，在粒子较少的情况下，我们的框架比以前的方法具有更高的效率。

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  • 来源
    《Multimedia Tools and Applications》 |2017年第1期|835-860|共26页
  • 作者

    Zhao Zhiqiang; Wang Tianjiang; Liu Fang; Choe Gwangmin; Yuan Caihong; Cui Zongmin;

  • 作者单位

    Huazhong Univ Sci & Technol, Sch Comp Sci & Technol, Intelligent & Distributed Comp Lab, Wuhan 430074, Peoples R China|Univ Jiujiang, Sch Informat Sci & Technol, Jiujiang 332005, Jiangxi, Peoples R China;

    Huazhong Univ Sci & Technol, Sch Comp Sci & Technol, Intelligent & Distributed Comp Lab, Wuhan 430074, Peoples R China;

    Huazhong Univ Sci & Technol, Sch Comp Sci & Technol, Intelligent & Distributed Comp Lab, Wuhan 430074, Peoples R China;

    Huazhong Univ Sci & Technol, Sch Comp Sci & Technol, Intelligent & Distributed Comp Lab, Wuhan 430074, Peoples R China;

    Huazhong Univ Sci & Technol, Sch Comp Sci & Technol, Intelligent & Distributed Comp Lab, Wuhan 430074, Peoples R China|Henan Univ, Sch Comp & Informat Engn, Kaifeng 475004, Henan, Peoples R China;

    Univ Jiujiang, Sch Informat Sci & Technol, Jiujiang 332005, Jiangxi, Peoples R China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Remarkable local resampling; Particle filtering; Visual tracking; Double threshold;

    机译：出色的本地重采样;粒子过滤;视觉跟踪;双阈值;

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