  Reliable and efficient monitoring of wild animals is crucial to inform
management and conservation decisions. The process of manually identifying
species of animals is time-consuming, monotonous, and expensive. Leveraging on
advances in deep learning and computer vision, we investigate in this paper the
efficiency of pre-trained models, specifically the VGG-16 and ResNet-50 model,
in identifying a male Kudu and a male Nyala in their natural habitats. These
pre-trained models have proven to be efficient in animal identification in
general. Still, there is little research on animals like the Kudu and Nyala,
who are usually well camouflaged and have similar features. The method of
transfer learning used in this paper is the fine-tuning method. The models are
evaluated before and after fine-tuning. The experimental results achieved an
accuracy of 93.2\% and 97.7\% for the VGG-16 and ResNet-50 models,
respectively, before fine-tuning and 97.7\% for both models after fine-tuning.
Although these results are impressive, it should be noted that they were taken
over a small sample size of 550 images split in half between the two classes;
therefore, this might not cater to enough scenarios to get a full conclusion of
the efficiency of the models. Therefore, there is room for more work in getting
a more extensive dataset and testing and extending to the female counterparts
of these species and the whole antelope species.

Computer Vision and Pattern Recognition;Computers and Society