| 基于粒子滤波和Mean-shift的跟踪算法 |
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| 引用本文： | 蒋 旻,许 勤,尚 涛,高伟义.基于粒子滤波和Mean-shift的跟踪算法[J].计算机工程,2010,36(5):21-22,2. |
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| 作者姓名： | 蒋 旻 许 勤 尚 涛 高伟义 |
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| 作者单位： | (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) |
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| 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。
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| 关 键 词： | 粒子滤波 Mean-shift算法 目标跟踪 |
Tracking Algorithm Based on Particle Filtering and Mean-shift |
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| JIANG Min,XU Qin,SHANG Tao,GAO Wei-yi.Tracking Algorithm Based on Particle Filtering and Mean-shift[J].Computer Engineering,2010,36(5):21-22,2. |
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| Authors: | JIANG Min XU Qin SHANG Tao GAO Wei-yi |
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| Affiliation: | (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) |
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| 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. |
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| Keywords: | particle filtering Mean-shift algorithm object tracking |
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