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Research Article | Open Access |

Estimation Method of the Total Number of Wild Animals based on Modified Jolly’s Method

Author 1: Kohei Arai Author 2: Takashi Higuchi Author 3: Tetsuya Murakami
International Journal of Advanced Computer Science and Applications (IJACSA) · Vol. 8, No. 1 · Published 2017

DOI: https://doi.org/10.14569/IJACSA.2017.080143

Abstract

Estimation method of the total number, the probabilities of birth and alive of wild animals based on Jolly’s method is proposed. Jolly’s method requires putting tags to the captured wild animals by bank trap while just identifications of the wild animals using camera images are required for the proposed method. An identification method is also proposed here. Other than these, the method for detection of specific wild animals is proposed. The proposed method is validated through simulations. The proposed method for specific wild animal detection with acquired camera images is also validated. The simulation results show that the proposed Modified Jolly’s Method: MJM is superior to the conventional Petersen method by 2.65% in terms of confidence interval of the estimated total number of wild pigs in the simulation cells in concern (128 by 128).

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How to Cite this Article

Arai, K., Higuchi, T., & Murakami, T. (2017). Estimation Method of the Total Number of Wild Animals based on Modified Jolly’s Method. International Journal of Advanced Computer Science and Applications, 8(1). https://doi.org/10.14569/IJACSA.2017.080143

Arai, Kohei, et al.. "Estimation Method of the Total Number of Wild Animals based on Modified Jolly’s Method." International Journal of Advanced Computer Science and Applications, vol. 8, no. 1, 2017, https://doi.org/10.14569/IJACSA.2017.080143.

@article{Arai2017,
  title     = {Estimation Method of the Total Number of Wild Animals based on Modified Jolly’s Method},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {8},
  number    = {1},
  year      = {2017},
  publisher = {The Science and Information Organization},
  author    = {Kohei Arai and Takashi Higuchi and Tetsuya Murakami},
  doi       = {10.14569/IJACSA.2017.080143},
  url       = {https://doi.org/10.14569/IJACSA.2017.080143}
}

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