Benjamin Evans

Benjamin Evans

Profile Display Name:
Benjamin Evans
E-mail Address:
Start Year

2018 (Cohort 5)

Research interests:
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PhD Project
PhD Title
A Deep Learning Model for Global Camera Trap Labelling
Research Theme
Biodiversity and Ecology
Primary Supervisor

Allan Tucker

Primary Institution
Secondary Supervisor

Chris Carbone

Secondary Institution

Recent years have seen an increase in camera-trap survey monitoring by ecological researchers. Camera-trap surveys collect imagery of medium-large mammal species across a region of interest. Dependent on the activity in an areas and number of camera trap days a survey’s conducted, millions of images may be captured. Currently researchers label each image with species and behavioural information or enlist Citizen Science volunteers.
Developments in machine learning, specifically deep learning has provided promising methods for general image recognition and may be utilised to assist in the camera trap labelling. Current models and methods that have been developed do not generalise well past the dataset they’re trained on.
The aim of the following research is to produce a technique that’s able to generalise to new data from around the world. We predict that introducing bio-geography into the classifier will increase the accuracy along with using multiple models trained for similar sub-species with an overarching model guiding which sub-species model should be used to classify.

Policy Impact
Background Reading


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