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Kernel density estimation and correntropy based background modeling and camera model parameter estimation for underwater video object detection

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Abstract

Underwater video object detection is challenging because of the complex background and the movement of the camera. In order to address this, we propose a novel scheme for simultaneously estimating the camera model parameters and detecting the object. The object detection phase includes background modeling and its learning. Background is modeled by the proposed spatial kernel density estimation (SKDE) model and the model learning happens in the SKDE feature space. Background modeling and its learning is pixel-based approach. The model histograms learn the new pixel through its histogram representation. Our learning and classification strategy is different from that of the algorithm proposed by Heikkila et al. in the year 2006 in the context of similarity measure. We have developed the correntropy-based similarity measure strategy that is used for model learning and pixel classification. The camera model parameters are estimated by 2D optimization method where we have used the corner features of an object at subpixel accuracy level. These subpixel level features are used in the pipelining framework for model parameter estimation. The estimated model parameters are used to transform the input frame, which in turn is used for model learning and classification. The proposed scheme has been tested with underwater video frames from six datasets. The efficacy of the proposed scheme is compared with seven existing schemes and it is found that the proposed scheme exhibits improved performance as compared to the existing methods.

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The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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Susmita Panda(\(1^{st}\) author) involved in formulation and development of algorithm, validating the algorithms with different datasets and manuscript preparation. Pradipta Kumar Nanda(\(2^{nd}\) author) involved in conceptualization of the problem, problem formulation, and validating results and manuscript.

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Correspondence to Susmita Panda.

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Panda, S., Nanda, P.K. Kernel density estimation and correntropy based background modeling and camera model parameter estimation for underwater video object detection. Soft Comput 25, 10477–10496 (2021). https://doi.org/10.1007/s00500-021-05919-7

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  • DOI: https://doi.org/10.1007/s00500-021-05919-7

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