³ÉÈË´óƬ

What cat is that? Victor Caquilpan's research project focused on using computer vision models to identify and track feral cats in camera trap images. Feral cats are a significant threat to the environment and accurately identifying them is crucial for conservation efforts. The study explores various methods including deep learning, feature extraction, and image processing to create an effective re-identification (Re-ID) model. The research systematically evaluates these models based on mAP and accuracy. By combining advanced computer vision and machine learning, this research aims to develop efficient solutions for feral cat RE-ID which can help monitor and manage feral cat populations and their impact on wildlife ecosystems.

Machine learning for ecology: Using a CNN to understand the impacts of linear infrastructures on microhylid frogs in Papuea New Guinea - Joseph Jantke's talk discussed cutting edge machine learning tools that have the potential to transform the scope and capabilities of ecological research, but the field has been slow to adopt new techniques. His research hopes to demonstrate the power of convolutional neural networks (CNN) for automating the classification of species in audio by analysing an audio dataset of Microhylid frogs in Papua New Guinea and comparing the resultant statistics and ecological conclusions with a previously conducted survey that did not use machine learning techniques.