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计算机工程 ›› 2010, Vol. 36 ›› Issue (5): 21-22,2. doi: 10.3969/j.issn.1000-3428.2010.05.008

• 博士论文 • 上一篇    下一篇

基于粒子滤波和Mean-shift的跟踪算法

蒋 旻1,许 勤1,尚 涛2,高伟义1   

  1. (1. 武汉科技大学计算机科学与技术学院,武汉 430081;2. 武汉科技大学信息科学与工程学院,武汉 430081)
  • 收稿日期:1900-01-01 修回日期:1900-01-01 出版日期:2010-03-05 发布日期:2010-03-05

Tracking Algorithm Based on Particle Filtering and Mean-shift

JIANG Min1, XU Qin1, SHANG Tao2, GAO Wei-yi1   

  1. (1. College of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan 430081; 2. College of Information Science and Engineering, Wuhan University of Science and Technology, Wuhan 430081)
  • Received:1900-01-01 Revised:1900-01-01 Online:2010-03-05 Published:2010-03-05

摘要: 粒子滤波作为一种基于贝叶斯估计的算法,在处理非线性运动目标跟踪问题上具有特殊的优势。基于此,提出一种基于粒子滤波和Mean-shift的混合跟踪算法(KMSEPF)。KMSEPF算法对一般的Mean-shift和粒子滤波混合算法进行改进。结果证明,KMSEPF算法与混合算法MSEPF相比,在计算效率提高的同时,跟踪准确性和处理遮挡的能力没有下降。

关键词: 粒子滤波, Mean-shift算法, 目标跟踪

Abstract: As an algorithm based on Bayesian estimation, particle filtering is predominant on tracking nonlinear moving target. This paper proposes an algorithm, which is based on Mean-shift and particle filtering, named K-means and Mean-shift Embedded Particle Filter(KMSEPF). The KMSEPF algorithm improves the general mixture algorithms which are based on particle filtering and Mean-shift. Results show that the algorithm reduces the computation complexity, while maintains the high precision and the ability to control the occlusion, compared with the MSEPF algorithm.

Key words: particle filtering, Mean-shift algorithm, object tracking

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