Recognition Of Endangered Pantanal Animal Species Using Deep Learning Methods

Pantanal is one of the most important biomes of the world, with a large number of wild animal species, some of them are in extinction. The automatic identification of wild animals is extremely important for the estimation of the species' population within Pantanal. However, digital processing techniques for the identification and tracking of species have faced great challenges due to clumsy light and pose conditions present in images taken in the wild. To overcome such problems, we propose a methodology that, by combining regular RGB images and thermal images, improves the identification of species even in images taken in rough circumstances. We use the SLIC segmentation algorithm to identify the regions of the images where animals are present; after that, we apply convolutional neural networks to classify the identified regions according to eight possible animal species. We experiment on a real-world dataset composed of 1,600 images. Our results showed an average gain between 6% and 10% when compared to the method Fast R-CNN.