BearID Project

ABOUT THE PROJECT

Combining Conservation Technology with Scientific Research

About

The conservation of nature is of global importance and scientists are responsible for monitoring changes in nature, from wildlife populations to landscapes. Tasked with this huge challenge, scientists are turning to new technologies to provide tools that aid in the collection and analysis of data required to better understand wildlife and monitor trends. Using machine learning techniques, we are developing software tools that can identify bears (Ursidae), starting with individual ID using face recognition. By combining this technique with remote camera trap imagery, we aim to provide a new survey technique for use in research and monitoring of wild bears. We anticipate a wide range of end users from scientists and managers, to governments, industries, and community scientists. Our research and software tool will provide a replicable technique and general approach that can be applied to other species beyond bears, which could aid conservation efforts worldwide.

Meet the team

Melanie Clapham, PhD

Conservation Scientist

Melanie Clapham is a Director and Conservation Scientist for the BearID Project. She holds a PhD in Conservation Biology from Lancaster University in the UK

Melanie Clapham, PhD

Ed Miller

Software Developer

Ed Miller is a Director and Software Developer for the BearID Project. He has 25+ years of experience developing hardware, software and systems.

Ed Miller

Mary Nguyen

Software Developer

Mary Nguyen is a Director and Software Developer for the BearID Project. Mary has 25+ years of experience in software development.

Mary Nguyen

Sophie Fowler

Research Assistant

Sophie Fowler began her work with the BearID Project as an undergraduate volunteer, and has since graduated with a BA in Geography from the University

Sophie Fowler

Research

We develop noninvasive technologies to identify and monitor bears, facilitating their conservation

Deep Learning

Deep learning is a form of machine learning, typically employing large neural networks to learn data representations from training data. Since 2012, all the winners of the premier computer vision contest, ImageNet Large Scale Visual Recognition Challenge, have utilized deep learning. Machine learning is already being applied to numerous wildlife classification and recognition problems.

Camera Traps

Camera traps have revolutionised the way we observe and study wildlife. From citizen science census projects to investigations into behavioural ecology, camera traps allow us to observe wildlife in a way not previously possible. Camera traps belong to a class of wildlife monitoring techniques referred to as ‘non-invasive’. They allow for data collection without humans being present and therefore are often considered as less stressful to individuals.

Applications for Bear Research

One challenge of using camera traps to study bears is the inability to consistently recognise individuals, due to the lack of unique natural markings for some species. Methods have been developed to try to account for a lack of individual identification in population inventory studies using cameras, but concerns over reliability remain. Automated methods to detect and identify both ‘marked’ and ‘unmarked’ wildlife in images and video footage are starting to receive increased interest.