Wildlife research projects worldwide could benefit from a new AI system which can automatically find, name, and follow individual animals in footage.
A University of Bristol team working on Animal Biometrics and AI for Conservation have been key contributors to the SA-FARI (Segment Anything in Footage of Animals for Recognition and Identification) project, developed by an international consortium with first author Dante Wasmuht and senior author Didac Suris, overall led by ConservationX Labs (CXL) and META .
SA-FARI builds on META's latest Segment Anything Model 3 (SAM3) which is a foundational and cutting-edge Vision-Language Model that is designed to use text and visual prompts to precisely identify, segment, and follow objects in images or videos.
This enables researchers to track animals in footage using 'masklets' which represent the exact outline of an animal in a video from frame-to-frame through time. It means the animal can be accurately separated from its background and form the basis of individual and behavioural analysis. This method has the potential to save thousands of hours for researchers using camera trap surveys in terms of viewing content manually.
The SA-FARI paper will be presented on Saturday 6 June at the Conference for Computer Vision and Pattern Recognition (CVPR) in Denver, USA, widely regarded as the leading conference for visual AI.
The paper has been selected as an Award Candidate at CVPR. For the Bristol team working on Animal Biometrics and AI Conservation, this marks a second consecutive year to get such a prestigious international nomination.
Tilo Burghardt , Professor of Computer Vision and Animal Biometrics at the University of Bristol, said: "Global problems require global solutions. Based on the group's pioneering track record of over 20 years, the University of Bristol is regarded as one of the go-to places for using AI for conservation in the UK and beyond, and is an important part of a growing international community working in this area."
Dr Otto Brookes, Lecturer in AI and Animal Biometrics from the Bristol team added: "The ability to locate animals in space and time is incredibly important for wildlife monitoring – it is a prerequisite for many tasks such as recognising behaviour and distinguishing individuals from one another and ultimately measuring how animals respond to conservation interventions."
The project trained and benchmarked an AI system which can automatically detect, name and track animals of nearly 100 species pixel-accurately in footage. To do this, a vast dataset of more than 11,000 wildlife videos taken in natural habitats was curated and annotated. The project offers this data freely downloadable for biologists, researchers, and conservationists to boost ecological projects worldwide with cutting-edge AI powers.
Professor Burghardt believes the SA-FARI work has the potential to be extended in the future by others by adding new features such as tracking animal body pose, depth and natural language descriptions.
The SA-FARI project was led by CXL and META, with important inputs from co-authors including Dr Otto Brookes and Prof Majid Mirmehdi from the University of Bristol, teams from the Hasso Plattner Institute, the University of Oviedo, Osa Conservation, the Senckenberg Museum of Natural History, the Max Planck Institute for Evolutionary Anthropology, and Climate Corridors – together the group pulled off a project of significant scale and truly inter-disciplinary reach.
Paper: " The SA-FARI Dataset: Segment Anything in Footage of Animals for Recognition and Identification ", by D. Francisco Wasmuht et al. CVPR 2026
