Self-Organizing Hierarchical Particle Swarm Optimization of Correlation Filters for Object Recognition

被引：13

作者：

Tehsin, Sara ^{[1
]}

Rehman, Saad ^{[1
]}

Bin Saeed, Muhammad Omer ^{[1
]}

Riaz, Farhan ^{[1
]}

Hassan, Ali ^{[1
]}

Abbas, Muhammad ^{[1
]}

Young, Rupert ^{[2
]}

Alam, Mohammad S. ^{[3
]}

机构：

[1] Natl Univ Sci & Technol, Dept Comp & Software Engn, Coll Elect & Mech Engn, Rawalpindi 44000, Pakistan

[2] Univ Sussex, Dept Engn & Design, Brighton BN1 9RH, E Sussex, England

[3] Texas A&M Univ, Kingsville, TX 78363 USA

来源：

IEEE ACCESS | 2017年 / 5卷

关键词：

Correlation filter; optimal trade-off; hierarchical particle swarm optimization; object recognition; TARGET DETECTION; ALGORITHMS;

D O I：

10.1109/ACCESS.2017.2762354

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Advanced correlation filters are an effective tool for target detection within a particular class. Most correlation filters are derived from a complex filter equation leading to a closed form filter solution. The response of the correlation filter depends upon the selected values of the optimal trade-off (OT) parameters. In this paper, the OT parameters are optimized using particle swarm optimization with respect to two different cost functions. The optimization has been made generic and is applied to each target separately in order to achieve the best possible result for each scenario. The filters obtained using standard particle swarm optimization (PSO) and hierarchal particle swarm optimization algorithms have been compared for various test images with the filter solutions available in the literature. It has been shown that optimization improves the performance of the filters significantly.

引用

页码：24495 / 24502

页数：8

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