Computer vision in animal behaviour: Markerless approaches to fine-scaled behaviour quantification in birds

Animals interact with the world through their behaviour. Over the last few decades, technological advancements have provided new ways for animals to be monitored and measured, offering novel insights into the study of animal behaviour. One such advance is the popularization of computer vision methods to measure the position, outline, posture, and behaviour of animals. While these methods have developed at an exponential rate over the last decade, uptake of methods for applications in animal behaviour is still limited, due to various challenges and obstacles that might have prevented computer vision innovations to be directly applied to biological studies. To overcome these challenges and better bridge the two fields, this thesis proposed a generalized framework for computer vision projects in animal behaviour applications, and demonstrated the key opportunities that can facilitate collaboration. As a demonstration of how this framework can be implemented, this thesis presented two main lines of research. The first is the development of multi-animal, markerless 3D posture estimation methods for fine-scaled behavioural quantification in birds, with the primary aim of measuring head rotation to estimate gaze and attention. The sceond is to solve the problem of automated behavioural coding in long-term study systems. With careful collaboration and development, computer vision has the potential to revolutionize the study of animal behaviour, while also transforming fields such as conservation, animal welfare, and beyond.