University of Sussex
Browse
08089340.pdf (3.87 MB)

Self-organizing hierarchical particle swarm optimization of correlation filters for object recognition

Download (3.87 MB)
Version 2 2023-06-12, 08:45
Version 1 2023-06-09, 08:40
journal contribution
posted on 2023-06-12, 08:45 authored by Sara Tehsin, Saad Rehman, Muhammad O Bin Saeed, Farhan Riaz, Ali Hassan, Rupert YoungRupert Young, Muhammad Abbas, Muhammad S Alam
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 (HPSO) 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.

History

Publication status

  • Published

File Version

  • Published version

Journal

IEEE Access

ISSN

2169-3536

Publisher

Institute of Electrical and Electronics Engineers

Volume

5

Page range

24495-24502

Department affiliated with

  • Engineering and Design Publications

Research groups affiliated with

  • Industrial Informatics and Signal Processing Research Group Publications

Full text available

  • Yes

Peer reviewed?

  • Yes

Legacy Posted Date

2017-11-06

First Open Access (FOA) Date

2017-11-07

First Compliant Deposit (FCD) Date

2017-11-06

Usage metrics

    University of Sussex (Publications)

    Categories

    No categories selected

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC