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基于自适应特征的地标跟踪算法
Landmark Tracking Algorithm Based on Adaptive Feature
查看参考文献7篇
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文摘
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为实现无人直升机的地标跟踪，将在线特征选择过程嵌入粒子滤波算法，采用自适应的状态转移模型，在跟踪过程中利用R、G、B值的线性组合作为候选特征集，对特征的目标区域和背景区域的颜色直方图分布进行统计，根据获得的对数似然比，选择区分度最好的特征计算似然图像，并通过2种途径获得2组粒子，用于估计目标位置。实验结果表明，该算法跟踪精度较高，鲁棒性较强 |
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其他语种文摘
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In order to realize landmark tracking by Unmanned Aerial Vehicle(UAV), this paper embeds online feature selection into particle filtering algorithm, and adopts adaptive transition model. Candidate feature set is composed of linear combination of R, G and B pixel values. Histograms of feature values for pixels on the object and in the background are computed for obtaining log likelihood ratio and variance ratio. The feature with the best discrimination is selected for computing the likelihood image. Two sets of particles are obtained via different approaches for estimating the position of the object. Experimental results show that the algorithm provides more reliable results and it is more robust |
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来源
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计算机工程
,2011,37(24):155-157 【核心库】
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关键词
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粒子滤波
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特征选择
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无人直升机
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目标跟踪
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地标
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地址
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浙江大学, 工业控制技术国家重点实验室, 杭州, 310027
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语种
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中文 |
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文献类型
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研究性论文 |
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ISSN
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1000-3428 |
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学科
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自动化技术、计算机技术 |
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基金
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国家863计划
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文献收藏号
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CSCD:4395123
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参考文献 共
7
共1页
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1.
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CSCD被引
224
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2.
Collins T. Online Selection of Discriminative Tracking Features.
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CSCD被引
1
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3.
Maggio E. Hybrid Particle Filter and Mean Shift Tracker with Adaptive Transition Model.
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CSCD被引
2
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4.
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CSCD被引
2
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5.
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1755
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6.
Shan Caifeng. Real-time Hand Tracking Using a Mean Shift Embedded Particle Filter.
Pattern Recognition,2007,40(7):1958-1970
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CSCD被引
22
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7.
Gu Yu. Embedding Ensemble Tracking in a Stochastic Framework for Robust Object Tracking.
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CSCD被引
1
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