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

Sara Tehsin; Saad Rehman; Muhammad Omer Bin Saeed; Farhan Riaz; Ali Hassan; Muhammad Abbas; Rupert Young; Mohammad S. Alam

doi:10.1109/ACCESS.2017.2762354

IEEE Access (Jan 2017)

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

Sara Tehsin,
Saad Rehman,
Muhammad Omer Bin Saeed,
Farhan Riaz,
Ali Hassan,
Muhammad Abbas,
Rupert Young,
Mohammad S. Alam

Affiliations

Sara Tehsin: ORCiD; Department of Computer and Software Engineering, College of Electrical and Mechanical Engineering, National University of Sciences and Technology, Rawalpindi, Pakistan
Saad Rehman: Department of Computer and Software Engineering, College of Electrical and Mechanical Engineering, National University of Sciences and Technology, Rawalpindi, Pakistan
Muhammad Omer Bin Saeed: Department of Computer and Software Engineering, College of Electrical and Mechanical Engineering, National University of Sciences and Technology, Rawalpindi, Pakistan
Farhan Riaz: Department of Computer and Software Engineering, College of Electrical and Mechanical Engineering, National University of Sciences and Technology, Rawalpindi, Pakistan
Ali Hassan: Department of Computer and Software Engineering, College of Electrical and Mechanical Engineering, National University of Sciences and Technology, Rawalpindi, Pakistan
Muhammad Abbas: Department of Computer and Software Engineering, College of Electrical and Mechanical Engineering, National University of Sciences and Technology, Rawalpindi, Pakistan
Rupert Young: Department of Engineering and Design, University of Sussex, Brighton, U.K.
Mohammad S. Alam: Texas A & M University, Kingsville, TX, USA

DOI: https://doi.org/10.1109/ACCESS.2017.2762354
Journal volume & issue: Vol. 5
pp. 24495 – 24502

Abstract

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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.

Published in IEEE Access

ISSN: 2169-3536 (Online)
Publisher: IEEE
Country of publisher: United States
LCC subjects: Technology: Electrical engineering. Electronics. Nuclear engineering
Website: https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6287639

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