Computational ethology for primate sociality: a novel paradigm for computer-vision-based analysis of animal behaviour - Schofield, DP (2022)
Research in the biological and wildlife sciences is increasingly reliant on video data for measuring animal behaviour, however large-scale analysis is often limited by the time and resources it takes to process video archives. Computer vision holds serious potential to unlock these datasets to analyse behaviour at an unprecedented level of scale, depth and reliability, however thus far a framework for processing and analysing behaviour from large-scale video datasets is lacking. This thesis attempts to solve this problem by developing the theory and methods for capturing long-term sociality of animal populations from longitudinal video archives, laying the foundations for an emerging field; computational ethology of animals in the wild. It makes several key contributions by a) establishing the first unified longitudinal video dataset of wild chimpanzee stone tool use across a 30 year period, and building a framework for collaborative research using cloud-technology b) developing a set of computational tools to allow for processing of large volumes of video data for automated individual identification and behaviour recognition c) applying these automated methods to validate use for social network analysis and d) measuring the social dynamics and behaviour of a group of wild chimpanzees living in the forest of Bossou, Guinea, West Africa.