Self-funded project: Tracking animal behaviour with UAVs/drones and machine learning

Application accepted for either MSc by Research or PhD.

Research areas: Animal behaviour; Deep learning; Machine learning; Computer vision
Modern UAV / drone technology offers a non-invasive approach to recording animals in the
wild. In ecology, the current state of the art in animal tracking identifies animals by their
contrast to the background. These methods are developed for use under laboratory
conditions with a uniform white background and even lighting, and it is
often impossible to detect animals from UAV footage in a natural habitat with a complex
background using current methods. However, recent advances in deep
machine learning have produced computer vision systems that can identify objects on any
background and under varied conditions. As such, modifying and applying
these new methods will allow us to study the collective behaviours of animals in their
natural environments.
This project will make used of modern deep-learning computer vision methods, such as
Mask RCNN to detect both positions of individuals and also generate a ‘segmentation
mask’ that identifies which pixels belong to each individual. The project will address current
weaknesses in the state-of-the-art algorithms and develop a new version of
the method which will then be applied to real animal UAV footage of killer whales and
banded mongooses to make discoveries about their social and collective behavior.
The ideal candidate would have a computer science or mathematics background, but have a
keen interest in interdisciplinary research and animal behaviour.

The Department of Biology at the University of York is committed to recruiting extraordinary future scientists regardless of age, ethnicity, gender, gender identity, disability, sexual orientation or career pathway to date. We understand that commitment and excellence can be shown in many ways and have built our recruitment process to reflect this. We welcome applicants from all backgrounds, particularly those underrepresented in science, who have curiosity, creativity and a drive to learn new skills.