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(BigData 2020) A Web Application for Feral Cat Recognition through Deep Learning

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Abstract: Deep learning has gained much attention and been applied in many different fields. In this paper, we present a web application developed to identify and detect the number of distinct feral cats of Australia using deep learning algorithms targeted to data captured from a set of remote sensing cameras. Feral cat recognition is an especially challenging application of deep learning since the cats are often similar and, in some cases, differ only in very small patterns on their fur. Given the automated, sensor-based image capture from remote cameras, further challenges relate to the limited number of images available. To tackle this, we train four neural network models to distinguish 75 classes (i.e. distinct feral cats) using 30 to 80 images for each class. Based on a range of evaluation metrics, we select Mask R-CNN model with ImageNet pre-trained weights augmented with the ResNet-50 network as the basis for the web application. Using images of cats from cameras in five different forests around Victoria, we achieved an average accuracy of identification of individual (distinct) cats of 89.4% with a maximum accuracy of 96.3%. This work is used to support ecologists in the School of Biosciences at the University of Melbourne.

Authors: Richard Sinnott (University of Melbourne, Australia)

Email: rsinnott66@gmail.com

Who can view: Everyone
Webinar Price: Free
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Professor Richard O. Sinnott is Professor of Applied Computing Systems at the University of Melbourne. He has a PhD in Computing Science, an MSc in Software Engineering and a BSc in Theoretical Physics (Hons). He is technical lead on a multitude of large-scale international projects with emphasis on security worth over $400m. This includes numerous projects in the biomedical domain and includes disease registries and clinical trials and observational studies involving thousands of patients. He has over 400 peer-reviewed publications across a range of computing and application-specific domains.
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