% pubman genre = article
@article{item_3575233,
title = {{PanAf20K: A large video dataset for wild ape detection and behaviour recognition (advance online)}},
author = {Brookes, Otto and Mirmehdi, Majid and Stephens, Colleen and Angedakin, Samuel and Corogenes, Katherine and Dowd, Dervla and Dieguez, Paula and Hicks, Thurston C. and Jones, Sorrel and Lee, Kevin and Leinert, Vera and Lapuente, Juan and McCarthy, Maureen and Meier, Amelia and Murai, Mizuki and Normand, Emmanuelle and Vergnes, Virginie and Wessling, Erin G. and Wittig, Roman M. and Langergraber, Kevin and Maldonado, Nuria and Yang, Xinyu and Zuberb{\"u}hler, Klaus and Boesch, Christophe and Arandjelovic, Mimi and K{\"u}hl, Hjalmar S. and Burghardt, Tilo},
language = {eng},
issn = {0920-5691; 1573-1405},
doi = {10.1007/s11263-024-02003-z},
year = {2024},
abstract = {{We present the PanAf20K dataset, the largest and most diverse open-access annotated video dataset of great apes in their natural environment. It comprises more than 7 million frames across {\textasciitilde} 20,000 camera trap videos of chimpanzees and gorillas collected at 18 field sites in tropical Africa as part of the Pan African Programme: The Cultured Chimpanzee. The footage is accompanied by a rich set of annotations and benchmarks making it suitable for training and testing a variety of challenging and ecologically important computer vision tasks including ape detection and behaviour recognition. Furthering AI analysis of camera trap information is critical given the International Union for Conservation of Nature now lists all species in the great ape family as either Endangered or Critically Endangered. We hope the dataset can form a solid basis for engagement of the AI community to improve performance, efficiency, and result interpretation in order to support assessments of great ape presence, abundance, distribution, and behaviour and thereby aid conservation efforts. The dataset and code are available from the project website: PanAf20K. {\copyright} The Author(s) 2024.}},
journal = {{International Journal of Computer Vision}},
}