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Occlusion-Aware Real-Time Object Tracking

delete2017-04-01
delete217
PRE
AI
X
Xingping Dong
沈
沈建冰 (Jianbing Shen) *
王文冠 封面图
王文冠 (Wenguan Wang)
J
Jianhong Liu
黄华 封面图
黄华 (Hua Huang)
DOI：10.1109/TMM.2016.2631884delete
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摘要

摘要

En 中文
在线学习方法因其对大多数视频序列的稳健性能而在视觉跟踪中很受欢迎。然而，由噪声更新引起的漂移问题对于大多数高度自适应的在线分类器仍然是一个挑战。在视觉跟踪中，目标对象外观变化，例如变形和长期遮挡，容易引起噪声更新。为了克服这个问题，引入了一种新的实时遮挡感知视觉跟踪算法。首先，我们学习了一种新颖的具有带核的循环结构的两级分类器，称为集成循环结构核 (ICSK)。第一阶段用于转换估计，第二阶段用于比例估计。循环结构使我们的算法实现了快速的学习和检测。然后，使用ICSK来检测没有遮挡的目标，并构建分类器池来保存这些带有噪声更新的分类器。当目标处于严重遮挡或长期遮挡后，我们根据熵最小化标准使用从分类器池中选择的最佳分类器对其进行重新检测。完整基准测试的大量实验结果表明，我们的实时算法比最先进的方法具有更好的性能。
Keyword:
Circulant structure
classifier-pool
entropy minimization
occlusion
real-time
visual tracking
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期刊

IEEE Transactions on Multimedia 封面图
IEEE Transactions on Multimedia
IF:
9.7
论文数:
4.6K
被引数:
2.4W

机构

B
beijing institute of technology
学者数:
5.5W
论文数: 4.0W
被引数: 63
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