自适应内容感知空间正则化相关滤波跟踪算法

doi:10.13229/j.cnki.jdxbgxb.20221518

Abstract

Abstract:

In order to solve the annoying boundary effects in correlation filter （CF） trackers induced by cyclic shift when sampling training patches， and improve the tracking performance， an adaptive content aware spatially regularized correlation filter （ACSRCF） is proposed. Firstly， real negative samples are generated from the background area around the target object， so as to alleviate the filter degradation induced by the fake negative samples generated from the circularly shifted object patches. Secondly， the locality sensitive histogram （LSH） based foreground feature is extracted and incorporated with the spatial regularization weight which is updated adaptively according to the varied object-oriented appearances. Thirdly， the CF model is optimized using the alternative direction method of multipliers （ADMM） in which the model is decomposed into two sub-problems and the LSH-based features are used in iteration for obtaining the optimal solutions. Finally， the proposed method is evaluated on 5 public benchmark datasets. The experimental results show that the accuracy and success rate scores of our method on OTB50 dataset are 90.3% and 66.1%， respectively， exceeding the other CF trackers. The data on the OTB100 dataset are 92.2% and 69.2%， and the accuracy ranks first among all the trackers， while the success rate is ahead of other CF trackers.

Key words: computer application, object tracking, correlation filter, adaptive spatial regularization, locality sensitive histogram

CLC Number:

TP391

Fa-sheng WANG,Bing HE,Fu-ming SUN,Hui ZHOU. Adaptive content aware spatially-regularized correlation filter for object tracking[J].Journal of Jilin University(Engineering and Technology Edition), 2024, 54(10): 3037-3049.

Figures/Tables 9

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Adaptive content aware spatially-regularized correlation filter for object tracking

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