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A Dataset and Application for Facial Recognition of Individual Gorillas in Zoo Environments

2020-12-08 19:23:22
Otto Brookes, Tilo Burghardt
arXiv_AI
         
Abstract

We put forward a video dataset with 5k+ facial bounding box annotations across a troop of 7 western lowland gorillas at Bristol Zoo Gardens. Training on this dataset, we implement and evaluate a standard deep learning pipeline on the task of facially recognising individual gorillas in a zoo environment. We show that a basic YOLOv3-powered application is able to perform identifications at 92% mAP when utilising single frames only. Tracking-by-detection-association and identity voting across short tracklets yields an improved robust performance of 97% mAP. To facilitate easy utilisation for enriching the research capabilities of zoo environments, we publish the code, video dataset, weights, and ground-truth annotations at this http URL.

Abstract (translated)

URL

https://arxiv.org/abs/2012.04689

PDF

https://arxiv.org/pdf/2012.04689.pdf

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