An active learning model to classify animal species in Hong Kong

Gareth Lamb, Ching Hei Lo, Jin Wu, Calvin K. F. Lee
3/23/2024
cs.CV

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Abstract

Camera traps are used by ecologists globally as an efficient and non-invasive method to monitor animals. While it is time-consuming to manually label the collected images, recent advances in deep learning and computer vision has made it possible to automating this process [1]. A major obstacle to this is the generalisability of these models when applying these images to independently collected data from other parts of the world [2]. Here, we use a deep active learning workflow [3], and train a model that is applicable to camera trap images collected in Hong Kong.

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Cite this paper

@article{lamb2024active,
  title  = {An active learning model to classify animal species in Hong Kong},
  author = {Gareth Lamb and Ching Hei Lo and Jin Wu and Calvin K. F. Lee},
  year   = {2024},
  eprint = {2403.15675},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2403.15675v1}
}

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