Methodology for mammal classification in camera trap images

Abstract

Using camera traps in animal ecology studies has increased because it facilitates the work of biologists and allows them to obtain information that otherwise would be impossible. A large number of photographs are capturing with this wildlife photography technique making difficult their posterior analysis. This paper presents a method to automatically identify the images with at least one animal and to classify them between birds and mammals. In this work a fuzzy classifier and a matched filter were used to identify the image with animals and to segment the images. An artificial neural network was employed to classify the segments between birds and mammals. We obtained a classification accuracy of 73.1% validating the model over real camera trap sessions. The database includes several difficulties, as the constant changes in the scene by climatic factors or animals partially occluded by the environment. This method was implemented in a software that is currently using in the Alexander von Humboldt Biological Resources Research Institute for studies of biodiversity in Colombia.