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Research on Anomaly Suppression Correlation Filtering Algorithm
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AI
DOI：10.1109/ACCESS.2021.3138080.png)
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摘要
En 中文
Aiming at the problems of target occlusion, illumination change, and fast motion in target tracking, an anomaly suppression correlation filtering algorithm is proposed. The histogram of oriented gradient (HOG) feature and color name (CN) feature are extracted to construct the target appearance model, and an abnormal suppression correlation filter is constructed to suppress sudden changes in the filter response graph. The alternating direction method of multipliers (ADMM) method is used to speed up the calculation of the filter. A scale filter is added in the article to solve the problem of inaccurate tracking caused by a single scale. In the model update process, the three-layer rotating circle memory model is used to update, which improves the tracking ability of the algorithm. It is better to update the target model in the scene where the target is occluded. The proposed algorithm and BACF, KCC, KCF, MKCFup, ARCF are tested in the OTB50, OTB100, UAV123, TC128 experimental data sets. The results show that the visual tracking algorithm proposed in the article has a high success rate and accuracy, and has certain research value.
Keyword:
Target tracking
Filtering algorithms
Correlation
Feature extraction
Training
Information filters
Real-time systems
Computer vision
target tracking
correlation filtering
abnormal suppression
ADMM method
memory model
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
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