Recurrence over Video Frames (RoVF) for the Re-identification of Meerkats
<|reference_start|>Recurrence over Video Frames (RoVF) for the Re-identification of Meerkats: 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.<|reference_end|>
@article{rogers2024recurrence,
    title={Recurrence over Video Frames (RoVF) for the Re-identification of
  Meerkats},
    author={Mitchell Rogers, Kobe Knowles, Ga"el Gendron, Shahrokh Heidari, David
  Arturo Soriano Valdez, Mihailo Azhar, Padriac O'Leary, Simon Eyre, Michael
  Witbrock, Patrice Delmas},
    journal={arXiv preprint arXiv:2406.13002},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.13002},
    primaryClass={cs.CV}
}