Description: The ability to automatically count animals may be essential for their survival. Out of all living mammals on Earth 60% are livestock, 36% humans, and only 4% are animals that live in the wild. In a relatively short period, human development of civilization caused a loss of 83% of all wildlife and 50% of all plants. The rate of species extinctions is accelerating. Wildlife surveys provide a population estimate and are conducted for various reasons such as species management, biology studies, and long term trend monitoring.
In this project, we have experimented with the use of deep learning (DL), together with aerial photography, to count the numbers of sea lions and African elephants with high precision, combining DL with density maps. Our approach shows better results than state-of-art DL models used for counting, indicating that the proposed method has the potential to be used more widely in large-scale wildlife surveying projects and initiatives.
Collaboration with: Jacob Kamminga (University of Twente)
Techniques used: Deep learning, aerial photography, visualizations, modelling.
Started: April 2020
Publications: Chirag Padubidri, Andreas Kamilaris, Savvas Karatsiolis and Jacob Hamminga, Counting Sea Lions and Elephants from Aerial Photography using Deep Learning with Density Maps, In Animal Biotelemetry journal, vol. 9, no. 27, August 2021. https://doi.org/10.1186/s40317-021-00247-x