Cheetah

Wildlife Animal Categorization

Wildlife animal categorization

To improve the categorization of wildlife animals caught in photo traps, deep-learning-based automatic image categorization is used in this project.

Problem Statement

Photo traps are an important information source for wildlife protection efforts. Cameras take thousands of pictures of wildlife animals which need to be categorized. Due to the large amount of images, this is a very time-consuming manual process.

Project Goal

The project goal is to automatically classify the wildlife images with the help of state-of-the-art deep learning models.

Project Approach

Together with wildlife experts, domain knowledge is used in the image annotation process (see also our annotation library ipannotator). With the high-quality annotated images, deep learning models are trained to recognize individual animals. Palaimon initiated this project, which is realized in cooperation with the Cheetah Conservation Fund (CFF) and the TU Berlin. Palaimon contributes both expert knowledge in the field of machine learning modeling and cloud-computing resources (GPUs).

Project Details

  • Project Duration: 02/2020 – running
  • Partner:
TU Berlin Cheetah Conservation Fund (CCF)
TU Berlin CCF