Recurrence over Video Frames (RoVF) for the Re-identification of Meerkats
| Autor: | Rogers, Mitchell, Knowles, Kobe, Gendron, Gaël, Heidari, Shahrokh, Valdez, David Arturo Soriano, Azhar, Mihailo, O'Leary, Padriac, Eyre, Simon, Witbrock, Michael, Delmas, Patrice |
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| Rok vydání: | 2024 |
| Předmět: | |
| Druh dokumentu: | Working Paper |
| Popis: | 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. Comment: Presented as a poster at the CV4Animals Workshop, CVPR 2024 |
| Databáze: | arXiv |
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