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A deep learning model for global camera trap labelling

Conservationists are increasingly using mobile “camera-traps” to monitor animal activity around the world, generating vast amounts of images and videos. After a field study has been conducted, each image / video is manually processed to identify what species are found. This process takes a lot of manual effort, some of which has been put to volunteers in citizen science projects such as Zooniverse. Work has begun on automating the labeling process with promising results and this project aims to use Artificial Intelligence to recognise species, integrate other data (including satellite images), and build spatial models of species to demonstrate how camera-traps can be used to better understand animal behaviour over large geographical regions.

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Meet the Principal Investigator(s) for the project

Dr Allan Tucker - Allan Tucker is Reader in the Department of Computer Science where he heads the Intelligent Data Analysis Group consisting of 17 academic staff, 15 PhD students and 4 post-docs. He has been researching Artificial Intelligence and Data Analytics for 21 years and has published 120 peer-reviewed journal and conference papers on data modelling and analysis. His research work includes long-term projects with Moorfields Eye Hospital where he has been developing pseudo-time models of eye disease (EPSRC - £320k) and with DEFRA on modelling fish population dynamics using state space and Bayesian techniques (NERC - £80k). Currently, he has projects with Google, the University of Pavia Italy, the Royal Free Hospital, UCL, Zoological Society of London and the Royal Botanical Gardens at Kew. He is academic lead on an Innovate UK, Regulators’ Pioneer Fund (£740k) with the Medical and Health Regulatory Authority on benchmarking AI apps for the NHS. He serves regularly on the PC of the top AI conferences (including IJCAI, AAAI, and ECML) and is on the editorial board for the Journal of Biomedical Informatics and Medical Informatics and Decision Making. He is hosting a special track on "Explainable AI" at the IEEE conference on Computer Based Medical Systems in 2019. He has been widely consulted on the ethical and practical implications of AI in health and medical research by the NHS, and the use of machine learning for modelling fisheries data by numerous government thinktanks and academia.

Related Research Group(s)

Intelligent Data Analysis

Intelligent Data Analysis - Concerned with effective analysis of data involving artificial intelligence, dynamic systems, image and signal processing, optimisation, pattern recognition, statistics and visualisation.


Project last modified 21/06/2021