Recurrence over Video Frames (RoVF) for the Re-identification of Meerkats
CoRR(2024)
摘要
Deep learning approaches for animal re-identification have had a major impact
on conservation, significantly reducing the time required for many downstream
tasks, such as well-being monitoring. We propose a method called Recurrence
over Video Frames (RoVF), which uses a recurrent head based on the Perceiver
architecture to iteratively construct an embedding from a video clip. RoVF is
trained using triplet loss based on the co-occurrence of individuals in the
video frames, where the individual IDs are unavailable. We tested this method
and various models based on the DINOv2 transformer architecture on a dataset of
meerkats collected at the Wellington Zoo. Our method achieves a top-1
re-identification accuracy of 49%, which is higher than that of the best
DINOv2 model (42%). We found that the model can match observations of
individuals where humans cannot, and our model (RoVF) performs better than the
comparisons with minimal fine-tuning. In future work, we plan to improve these
models by using pre-text tasks, apply them to animal behaviour classification,
and perform a hyperparameter search to optimise the models further.
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