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Scale-Adaptive Kernel Correlation Filter with Maximum Posterior Probability Estimation and Combined Features for Visual Target Tracking
IEEJ Transactions on Electrical and Electronic Engineering ( IF 1.0 ) Pub Date : 2021-06-14 , DOI: 10.1002/tee.23404
Jingxiang Xu 1 , Xuedong Wu 1 , Baiheng Cao 1
Affiliation  

Although the correlation filtering tracker for visual target tracking has achieved excellent results in both accuracy and robustness, there are still some problems yet to be solved. Obtaining stable scale estimation using traditional trackers is a challenging problem in visual target tracking, and many trackers fail to handle scale change in complex video sequences. In order to solve the problems of scale change, partial occlusion and geometric deformation for target tracking effectively, a new tracker based on kernel correlation filtering is developed in our study. The tracker obtained with maximum posterior probability method has scale adaptive ability and can deal with scale change to improve the tracking ability. In addition, the tracker further enhances the ability to deal with illumination variation, geometric deformation and occlusion by fusing the adaptive color naming feature and the histogram of oriented gradient feature as well. The VOT-2018 which has 50 video sequences is used as the benchmark data set in this work and the simulation evaluation on this data set have shown that the proposed tracker has achieved stable tracking results in some challenging scenarios and can achieve better tracking performance than other trackers. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

中文翻译:

用于视觉目标跟踪的具有最大后验概率估计和组合特征的尺度自适应核相关滤波器

虽然用于视觉目标跟踪的相关滤波跟踪器在精度和鲁棒性方面都取得了优异的成绩,但仍有一些问题有待解决。使用传统跟踪器获得稳定的尺度估计是视觉目标跟踪中的一个具有挑战性的问题,许多跟踪器无法处理复杂视频序列中的尺度变化。为了有效解决目标跟踪中尺度变化、局部遮挡和几何变形等问题,本文开发了一种基于核相关滤波的跟踪器。最大后验概率法得到的跟踪器具有尺度自适应能力,可以处理尺度变化,提高跟踪能力。此外,跟踪器进一步增强了处理光照变化的能力,通过融合自适应颜色命名特征和定向梯度特征的直方图来实现几何变形和遮挡。在这项工作中使用具有 50 个视频序列的 VOT-2018 作为基准数据集,对该数据集的仿真评估表明,所提出的跟踪器在一些具有挑战性的场景中取得了稳定的跟踪结果,并且可以实现比其他跟踪器更好的跟踪性能跟踪器。© 2021 日本电气工程师学会。由 Wiley Periodicals LLC 出版。在这项工作中使用具有 50 个视频序列的 VOT-2018 作为基准数据集,对该数据集的仿真评估表明,所提出的跟踪器在一些具有挑战性的场景中取得了稳定的跟踪结果,并且可以实现比其他跟踪器更好的跟踪性能跟踪器。© 2021 日本电气工程师学会。由 Wiley Periodicals LLC 出版。在这项工作中使用具有 50 个视频序列的 VOT-2018 作为基准数据集,对该数据集的仿真评估表明,所提出的跟踪器在一些具有挑战性的场景中取得了稳定的跟踪结果,并且可以实现比其他跟踪器更好的跟踪性能跟踪器。© 2021 日本电气工程师学会。由 Wiley Periodicals LLC 出版。
更新日期:2021-07-16
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