基于多核融合的目标大面积遮挡处理方法

肖鹏; 段洣毅; 赵琪

基于多核融合的目标大面积遮挡处理方法

1.
北京航空航天大学, 计算机学院, 北京 100191
2. 北京图形研究所, 北京 100029

基金项目: 国家自然科学基金资助项目(61005084)

详细信息

中图分类号: TP391
计量
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- 被引次数: 0
出版历程
- 收稿日期: 2011-03-22
- 网络出版日期: 2012-06-30

Occlusion handling approach in visual tracking based on multiple-kernel fusion

1.
School of Computer Science and Technology, Beijing University of Aeronautics and Astronautics, Beijing 100191, China
2. Beijing Institute of Graphics, Beijing 100029, China

摘要

摘要: 提出了一种基于多核融合的目标遮挡处理方法,用于提高大面积遮挡情况下视觉目标跟踪算法的鲁棒性和准确性.与现有基于单个对称核加权直方图的mean shift跟踪算法不同,该方法以目标区域内的多个非中心位置为核函数中心,构建多个非对称核加权直方图.由于这些直方图对目标的不同区域赋予了不同的权重,使得在遮挡发生时总存在一些直方图受影响较小.依据各个直方图分别进行mean shift迭代获得一组目标位置估计后,利用D-S证据理论融合判定最终的目标位置.实验结果表明,该方法在目标被大面积遮挡时仍能够获得准确的跟踪.
- 目标跟踪 /
- 视觉跟踪 /
- 均值漂移 /
- 遮挡处理 /
- 多核 /
- 证据理论
Abstract: A novel visual tracking approach based on multiple-kernel fusion was proposed to improve robustness and accuracy of tracking under large-area occlusion. Unlike traditional single symmetric kernel weighted histogram used in mean shift tracking, this approach adopted several asymmetric kernel functions centered at different positions within target region to build a set of asymmetric kernel weighted histograms. Because these histograms weighted each part of the target region differently, there must be some less influenced histograms during occlusion. Based on each histogram, a set of target location estimations were provided respectively by mean shift iteration, and the target location was obtained by fusing these estimations using Dempster-Shafer evidence theory. The experimental results demonstrate the effectiveness of the proposed approach under large-area occlusion.
- target tracking /
- visual tracking /
- mean shift /
- occlusion handling /
- multiple kernels /
- evidence theory

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参考文献(11)

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