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arXiv · 2012.04689

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

Otto Brookes·Tilo Burghardt
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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 data.bris.ac.uk.

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Otto Brookes, Tilo Burghardt. 2020-12-08. A Dataset and Application for Facial Recognition of Individual Gorillas in Zoo Environments. https://arxiv.org/abs/2012.04689

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