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

Tehsin, Sara, Rehman, Saad, Bin Saeed, Muhammad O, Riaz, Farhan, Hassan, Ali, Young, Rupert, Abbas, Muhammad and Alam, Muhammad S (2017) Self-organizing hierarchical particle swarm optimization of correlation filters for object recognition. IEEE Access, 5. pp. 24495-24502. ISSN 2169-3536

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Abstract

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.

Item Type: Article
Keywords: Correlation filter, Optimal trade-off, Hierarchical particle swarm optimization, Object recognition
Schools and Departments: School of Engineering and Informatics > Engineering and Design
Research Centres and Groups: Industrial Informatics and Signal Processing Research Group
Subjects: Q Science > QA Mathematics > QA0075 Electronic computers. Computer science
T Technology > TA Engineering (General). Civil engineering (General) > TA0329 Engineering mathematics. Engineering analysis
Depositing User: Rupert Young
Date Deposited: 06 Nov 2017 17:07
Last Modified: 11 Dec 2017 12:37
URI: http://sro.sussex.ac.uk/id/eprint/70957

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