期刊文献+

基于颜色的粒子滤波算法的改进与全硬件实现 被引量:3

An Improved Color-based Particle Filter and Its Full Hardware Implementation
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摘要 传统基于颜色的粒子滤波算法在硬件实现中存在着跟踪效果不理想、实时性差等问题。该文结合硬件电路需要对基于颜色的粒子滤波算法进行了改进,在传统SR重采样算法的基础上将剩余粒子撒向目标点附近,以提高其在硬件环境下跟踪的准确性与稳定性。文中给出了改进算法的全硬件实现的电路架构,并在FPGA上完成了目标跟踪系统的实现。实验表明提出改进算法与硬件实现方案对简单背景环境下运动目标有着良好的跟踪效果,系统实时处理能力可达72 FPS,而硬件消耗为7387个逻辑单元。为进一步适应复杂的应用环境,在此粒子滤波器基础上给出了可扩展的分布式粒子滤波系统的架构以及相应的重采样策略,良好的并行性与可扩展性使得系统能够完成复杂背景环境下多特征、多目标的跟踪任务。 Nowadays,the research on the implementation of color-based particle filters is facing the problem of tracking accuracy and real-time processing speed.This paper presents a modified color-based particle filter,which basing on the traditional SR resample algorithm,scatters the left particles causing by hardware circuits around the target.Results show that proposed particle filters improve the accuracy and robustness of object tracking.Moreover,the architecture of its full hardware implementation is depicted in the paper.The experimental study on FPGA indicates that the proposed color-based particle filters perform robust tracking at 72 FPS and with 7387 LEs(Logic Element) hardware cost.In addition to that,the framework of scalable distributed particle filters and its resample scheme are presented to adapt more complex scenarios,carrying out multi-feature and multi-target tracking.
出处 《电子与信息学报》 EI CSCD 北大核心 2011年第2期448-454,共7页 Journal of Electronics & Information Technology
基金 国家自然科学基金(60720106003) 国家863计划项目(2009AA011706)资助课题
关键词 目标跟踪 粒子滤波 实时性 可扩展性 Object tracking Particle filter Real-time Scalability
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参考文献14

  • 1Arulampalam M S, Maskell S, and Gordon N, et al.. A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Transactions on Signal Processing, 2002, 50(2): 174-188.
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二级参考文献10

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共引文献10

同被引文献29

  • 1胡士强,敬忠良.粒子滤波算法综述[J].控制与决策,2005,20(4):361-365. 被引量:297
  • 2许东,徐文立.利用结构模板对运动目标进行跟踪[J].电子与信息学报,2005,27(7):1021-1024. 被引量:2
  • 3张琪,胡昌华,乔玉坤.基于权值选择的粒子滤波算法研究[J].控制与决策,2008,23(1):117-120. 被引量:45
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  • 8Medeiros H,Park J,Kak A.A parallel color-based particle filter for object tracking[C]//IEEE Computer Society Conference on Computer Vision and Pattern Recognition,Alaska,2008:1-8.
  • 9Wang Tzu-Heng,Chang Jing-Ying,Chen Liang-Gee.Algorithm and architecture for object tracking using particle filter[C]//IEEE International Conference on Multimedia and Expo,New York,2009:1374-1377.
  • 10Deng Minxi,Guan Qing,Xu Sheng.Intelligent video tar-get tracking system based on DSP[C]//International Conference on Computational Problem-Solving,2010.

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