Overview
Badgers are short-legged omnivores and wild animals, and their existence is in danger in some parts of the world. The Badger dataset is a small sample subset of videos of different badgers collected by the foundation of Das and Boom. The dataset contains four individual instances of badgers with a total number of 51 videos (1641 frames containing the existence of badgers). The badger classes (identities) are: badger_esp, badger_iaco, badger_looi, and badger_strik. The badgers were recorded in 2016 and 2017 at the Badger Rescue Center of Das & Boom in the Netherlands. Additionally, some videos and photos were made at release locations for badger rehabilitation purposes. To identify each badger, they are micro-chipped, so the animal can be tracked during captivity and identified after release. The streaming lengths (Ts) of the videos vary in the range between 15 and 60 s. We extracted approximately a frame per second, for which we developed a script that extracts (Ts ± 2) video frames. We remark that some frames do not contain the existence of badgers and such frames are not used in our experiments. A detailed descriptions of the dataset splits can be found in the paper Additionally, the entire dataset and meta descriptions of the bounding boxes in the train-test splits can be accessed from this link (download dataset (subset)).

Publication

• "Okafor, E., Berendsen, G., Schomaker, L., & Wiering, M. (2018, October). Detection and Recognition of Badgers Using Deep Learning. In International Conference on Artificial Neural Networks (pp. 554-563). Springer, Cham."

Citation

@inproceedings{okafor2018detection,
  title={Detection and Recognition of Badgers Using Deep Learning},
  author={Okafor, Emmanuel and Berendsen, Gerard and Schomaker, Lambert and Wiering, Marco},
  booktitle={International Conference on Artificial Neural Networks},
  pages={554--563},
  year={2018},
  organization={Springer}
}