基于多模型蒸馏的时间正则化相关滤波跟踪算法

doi:10.12305/j.issn.1001-506X.2022.08.08

Abstract

Abstract:

At present, most correlation filter based tracking methods adopts simple linear weighted fusion of the model or use the historical model as the temporal regularization term to constrain the model update, which can enhance the ability of the filter to discriminate the target. However, this method cannot make full use of the information of the target, which is easy to cause model degradation and drift. This paper proposes a temporal regularized correlation filter based on multi-model distillation for visual tracking. This method collects the independent model generated by the current sample in the tracking process, which can guide the filter update in the local sample library containing background information. This can retain the robust features of the target in the temporal domain. At the same time, the reliability weight is updated according to the different characterization ability of each model for the current target. Finally, the alternating direction multiplier (ADMM) algorithm is used to iteratively optimize the model. A large number of experimental results in the databases show that the precision and success rate of the method have been greatly improved.

Key words: target tracking, correlation filter, distillation learning, temporal regularization

CLC Number:

TN919.82

Zhuling QIU, Yufei ZHA, Zhenyu LI, Yuming LI, Peng ZHANG, Chuan ZHU. Temporal regularized correlation filter tracking algorithm based on multi-model distillation[J]. Systems Engineering and Electronics, 2022, 44(8): 2448-2456.

Figures/Tables 9

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References 30

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Trackers	AUC/%	DP/%	Speed/FPS
baseline	45.9	66.0	35
baseline_sample	47.1	68.3	31
baseline_FWM	47.9	70.2	29
baseline_UWM	47.3	68.8	28
baseline_PUWM	48.7	72.5	25
Our	51.8	75.5	20

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Temporal regularized correlation filter tracking algorithm based on multi-model distillation

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