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MRF Model-Based Estimation of Camera Parameters and Detection of Underwater Moving Objects

MRF Model-Based Estimation of Camera Parameters and Detection of Underwater Moving Objects

Susmita Panda, Pradipta Kumar Nanda
Copyright: © 2020 |Volume: 14 |Issue: 4 |Pages: 29
ISSN: 1557-3958|EISSN: 1557-3966|EISBN13: 9781799805342|DOI: 10.4018/IJCINI.2020100101
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MLA

Panda, Susmita, and Pradipta Kumar Nanda. "MRF Model-Based Estimation of Camera Parameters and Detection of Underwater Moving Objects." IJCINI vol.14, no.4 2020: pp.1-29. http://doi.org/10.4018/IJCINI.2020100101

APA

Panda, S. & Nanda, P. K. (2020). MRF Model-Based Estimation of Camera Parameters and Detection of Underwater Moving Objects. International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), 14(4), 1-29. http://doi.org/10.4018/IJCINI.2020100101

Chicago

Panda, Susmita, and Pradipta Kumar Nanda. "MRF Model-Based Estimation of Camera Parameters and Detection of Underwater Moving Objects," International Journal of Cognitive Informatics and Natural Intelligence (IJCINI) 14, no.4: 1-29. http://doi.org/10.4018/IJCINI.2020100101

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Abstract

The detection of underwater objects in a video is a challenging problem particularly when both the camera and the objects are in motion. In this article, this problem has been conceived as an incomplete data problem and hence the problem is formulated in expectation maximization (EM) framework. In the E-step, the frame labels are the maximum a posterior (MAP) estimates, which are obtained using simulated annealing (SA) and the iterated conditional mode (ICM) algorithm. In the M-step, the camera model parameters, both intrinsic and extrinsic, are estimated. In case of parameter estimation, the features are extracted at coarse and fine scale. In order to continuously detect the object in different video frames, EM algorithm is repeated for each frame. The performance of the proposed scheme has been compared with other algorithms and the proposed algorithm is found to outperform.