We put forward a video dataset with 5k+ facial bounding box annotations across a troop of 7 western lowland gorillas (Gorilla gorilla gorilla) 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 at 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.
|Publication status||Published - 11 Jan 2021|
|Event|| International Conference on Pattern Recognition (ICPR) Workshop on Visual Observation and Analysis of Vertebrate And Insect Behavior - Milan, Italy|
Duration: 10 Jan 2021 → 15 Jan 2021
Conference number: 25
|Conference||International Conference on Pattern Recognition (ICPR) Workshop on Visual Observation and Analysis of Vertebrate And Insect Behavior|
|Period||10/01/21 → 15/01/21|