Joint spatial reliability and correlation filter learning for visual tracking

被引：0
|
作者：
Zhang F. [1 ]
Ma S. [1 ]
Zhang L. [1 ]
He L. [1 ]
Qiu Z. [2 ]
Han Y. [2 ]
机构：
[1] School of Aeronautical Engineering, Air Force Engineering University, Xi'an
[2] Graduate School, Air Force Engineering University, Xi'an
来源：
Xi'an Dianzi Keji Daxue Xuebao/Journal of Xidian University | 2021年 / 48卷 / 05期
关键词：
Adaptive learning; Correlation filter; Joint learning; Perceptual hashing algorithm; Spatial reliability; Visual tracking;
D O I：
10.19665/j.issn1001-2400.2021.05.020
中图分类号：
学科分类号：
摘要：
The discriminant correlation filter (DCF) uses the cyclic shift to generate negative samples, which inevitably brings boundary effects. The background-aware correlation filter (BACF) attempts to use the clipping matrix to obtain more real negative samples. The method can not only effectively alleviate the influence of the boundary effect, but also enhance the learning of background information. However, the use of the clipping matrix lacks the learning of the spatial reliability of different positions, which may cause the background information to dominate the learning of the filter. In order to solve this problem, this paper introduces the learning of spatial reliability into the correlation filter. And the Alternate Direction Method is used to iteratively obtain the solution of spatial reliability and the filter. Our method can strengthen the learning of the spatial reliability region and enhance the filter's ability to discriminate targets and background. In addition, in order to optimize the model update strategy, an adaptive model update method based on the Perceptual Hash Algorithm is proposed, which improves the effectiveness of filter learning. The proposed algorithm has been comprehensively evaluated on standard visual tracking datasets. The results verify the effectiveness and real-time performance of the algorithm. © 2021, The Editorial Board of Journal of Xidian University. All right reserved.
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页码：167 / 177
页数：10
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