基于核的无标度网络模型视觉跟踪

Risheng Han
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引用次数: 0

摘要

提出了一种基于核目标跟踪和无标度网络模型的跟踪算法。该方法将目标模型视为无标度复杂网络模型的度分布。利用无标度网络的特性,可以利用目标的特殊像素对目标的跟踪效果比其他像素大得多。基于这些特殊点,可以生成目标网络,也可以更新目标规模和目标模型。实验表明,该算法可以在杂波背景、变尺度和部分遮挡条件下保持目标跟踪。本文首次研究了如何将复杂网络模型应用到视觉跟踪算法中。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Kernel Based Visual Tracking with Scale-Free Network Model
A novel tracking algorithm is proposed based on kernel based object tracking and scale-free network model. The proposed method regards target model as degree distribution of scale-free complex network model. By taking advantage of scale-free network's characteristic, target's special pixels which have much larger effects than other pixels can be used in tracking process. Based on these special points, target's network can be produced and target scale and target model can also be updated. Experiments show that the proposed algorithm can keep tracking target under conditions of clutter background, varying scales, and partial occlusion. This is the first research about how to use complex network model into the visual tracking algorithm.
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