Removing Human Bottlenecks in Bird Classification Using Camera Trap Images and Deep Learning

no code implementations • 3 May 2023 • Carl Chalmers, Paul Fergus, Serge Wich, Steven N Longmore, Naomi Davies Walsh, Philip Stephens, Chris Sutherland, Naomi Matthews, Jens Mudde, Amira Nuseibeh

In this paper, we outline an approach for overcoming these issues by utilising deep learning for real-time classi-fication of bird species and automated removal of false positives in camera trap data.

Key Detection Specificity

Empowering Wildlife Guardians: An Equitable Digital Stewardship and Reward System for Biodiversity Conservation using Deep Learning and 3/4G Camera Traps

no code implementations • 25 Apr 2023 • Paul Fergus, Carl Chalmers, Steven Longmore, Serge Wich, Carmen Warmenhove, Jonathan Swart, Thuto Ngongwane, André Burger, Jonathan Ledgard, Erik Meijaard

Each time an animal was captured in a camera and successfully classified, 1 penny (an arbitrary amount - mechanisms still need to be developed to determine the real value of species) was transferred from the animal account to its associated guardian.

Conservation AI: Live Stream Analysis for the Detection of Endangered Species Using Convolutional Neural Networks and Drone Technology

no code implementations • 16 Oct 2019 • C. Chalmers, P. Fergus, Serge Wich, Aday Curbelo Montanez

Using a robust deep learning pipeline, a convolutional neural network is trained and implemented to detect rhinos and cars (considered an important tool in poaching for fast access and artefact transportation in natural habitats) in the study, that are found within live video streamed from drones Transfer learning with the Faster RCNN Resnet 101 is performed to train a custom model with 350 images of rhinos and 350 images of cars.

Transfer Learning