A Deep Learning Model for Global Camera Trap Labelling

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 area and the 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 to assist in doing so.
Developments in machine learning, specifically Deep Learning, has provided promising image recognition methods and may be utilised to assist in the camera trap labelling.
The research aims to produce a technique that allows ecological researchers to go from raw images through animal detection, species classification, and analysis identifying species behaviours based on the underlying predictions, all as an automated process.