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Evolutionary correlation filtering based on pseudo-bacterial genetic algorithm for pose estimation of highly occluded targets

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Abstract

An accurate method based on evolutionary correlation filtering to solve pose estimation of highly occluded targets is presented. The proposed method performs multiple correlation operations between an input scene and a bank of filters designed in frequency-domain. Each filter is computed with statistical parameters of a real-world scene and a template that contains information of the target in a single pose parameter configuration. A vast set of templates is generated from multiple views of a three-dimensional model of the target, which are created synthetically with computer graphics. An evolutionary approach in the bank of filter construction for optimizing the pose estimation parameters is implemented. The evolutionary computation technique based on a pseudo-bacterial genetic algorithm yields high estimation accuracy finding the best filter that produces the highest matching score. The proposed evolutionary correlation filtering yields good convergence of the bank of filter optimization, which produces a reduction of the number of computational operations. Experimental results demonstrate the robustness of the proposed method in terms of detection performance and pose estimation of highly occluded targets compared with state-of-the-art methods.

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Acknowledgements

This work was supported by the Coordinación Institucional de Investigación of CETYS Universidad, and by Consejo Nacional de Ciencia y Tecnología (CONACYT).

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  1. Centro de Innovación y Diseño (CEID), CETYS Universidad, Ave. CETYS Universidad No. 4, El Lago, Tijuana, B.C., 22210, México

    Kenia Picos & Ulises Orozco-Rosas

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Correspondence to Kenia Picos.

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Picos, K., Orozco-Rosas, U. Evolutionary correlation filtering based on pseudo-bacterial genetic algorithm for pose estimation of highly occluded targets. Multimed Tools Appl 80, 23051–23072 (2021). https://doi.org/10.1007/s11042-020-08991-7

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