| 基于均值移动与粒子滤波融合的跟踪算法 |
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| 引用本文： | 王华冰,杨盈昀,张拉,王子微.基于均值移动与粒子滤波融合的跟踪算法[J].计算机工程,2012,38(14):150-152. |
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| 作者姓名： | 王华冰 杨盈昀 张拉 王子微 |
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| 作者单位： | 中国传媒大学信息工程学院,北京,100024 |
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| 基金项目： | 国家自然科学基金资助项目，中国传媒大学“211”工程三期重点学科建设基金资助项目 |
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| 摘 要： | 针对均值移动算法鲁棒性差以及粒子滤波算法计算量大、难以满足实时跟踪的特点，提出2种先均值移动后粒子滤波的融合算法，分别为粒子数目保持恒定的融合算法和粒子数目自适应的融合算法。实验结果证明，与已有算法相比，2种算法在实时性提高的同时，跟踪准确性和抗干扰能力没有明显下降。
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| 关 键 词： | 目标跟踪 均值移动 粒子滤波 融合算法 自适应 实时跟踪 |
| 收稿时间： | 2011-09-14 |
Tracking Algorithm Based on Fusion of Mean-shift and Particle Filtering |
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| WANG Hua-bing
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YANG Ying-yun
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ZHANG La
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WANG Zi-wei.Tracking Algorithm Based on Fusion of Mean-shift and Particle Filtering[J].Computer Engineering,2012,38(14):150-152. |
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| Authors: | WANG Hua-bing YANG Ying-yun ZHANG La WANG Zi-wei |
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| Affiliation: | (School of Information Engineering,Communication University of China,Beijing 100024,China) |
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| Abstract: | This paper proposes two fusion algorithms based on poor robustness of Mean-shift algorithm and large computation of particle filtering algorithm.Both of the new algorithms take Mean-shift first and particle filtering later.The number of particles is constant and the number of particles is adaptive.Experimental results show that compared with existing algorithms,the real-time of new algorithms is improved,while tracking accuracy and anti-interference ability without significant decline. |
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| Keywords: | object tracking Mean-shift particle filtering fusion algorithm self-adaptive real-time tracking |
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