Counting animals in ecological images

In the field of ecology, counting of animals to estimate population size and prey abundance is important for the conservation of wildlife. This involves analyzing large volumes of image, video or audio data and manual counting. Automating the process of counting animals would be invaluable to researchers as it will eliminate the tedious time-consuming task of counting. The purpose of this dissertation is to address manual counting in images by implementing an automated solution using computer vision. This research applies a blob detection algorithm primarily based on the determinant of the Hessian matrix to estimate counts of animals in aerial images of colonies in a user-friendly web application and trains an object detection model using deep convolutional neural networks to automatically identify and count penguin prey in 2053 images extracted from animal-borne videos. The blob detection algorithm reports an average relative bias of less than 6% and the YOLOv3 object detection model automatically detects jellyfish, school of fish and fish with a mean average precision of 82,53% and counts with an average relative bias of -17,66% over all classes. The results show that applying traditional computer vision methods and deep learning on data-scarce and data-rich situations respectively, can save ecologists an immense amount of time used on manual tedious methods of analysis and counting. Additionally, these automated counting methods can contribute towards improving wildlife conservation and future studies