Improved Automated Recognition of Leopards from Photographs

Jacobie Mouton; Lynette Van Zijl; Matthew P.E. Schurch

doi:10.3957/056.050.0197

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Jacobie Moutonhttps://orcid.org/0000-0003-3067-5175,^1,* Lynette Van Zijlhttps://orcid.org/0000-0001-5735-0448,¹ Matthew P.E. Schurchhttps://orcid.org/0000-0002-3507-227X^2,3,4

¹Department of Computer Science, Stellenbosch University, Stellenbosch, South Africa
²Kgalagadi Leopard Project, South Africa
³Landmark Foundation, Riversdale, South Africa
⁴Department of Biodiversity and Conservation Biology, University of Western Cape, Bellville, South Africa

^*To whom correspondence should be addressed. E-mail: 21054479@sun.ac.za

Abstract

Two improvements on the automated image recognition of individual leopards (Panthera pardus) are presented. Firstly, a new method of automated segmentation on images, with a guaranteed side view of the animal, allows for a region of interest to be calculated automatically, reducing the manual effort of the image processing substantially. Secondly, improvements on the automated recognition of citizen science images with extreme viewpoints are detailed.

Citation Download Citation

Jacobie Mouton, Lynette Van Zijl, and Matthew P.E. Schurch "Improved Automated Recognition of Leopards from Photographs," African Journal of Wildlife Research 50(1), (2 December 2020). https://doi.org/10.3957/056.050.0197

Received: 5 August 2020; Accepted: 18 November 2020; Published: 2 December 2020

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