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Self-organizing hierarchical particle swarm optimization of correlation filters for object recognition
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 AlamAdvanced 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 AccessISSN
2169-3536Publisher
Institute of Electrical and Electronics EngineersExternal DOI
Volume
5Page range
24495-24502Department 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