DOI: 10.5523/bris.1h73erszj3ckn2qjwm4sqmr2wt
title: PanAf20K AI-enabled Video Dataset of Great Apes in the Wild
title: A Large Video Dataset for Wild Ape Detection and Behaviour Recognition

description: DESCRIPTION. We present the PanAf20K dataset, at the time of publication 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 ~20,000 camera trap videos of chimpanzees and gorillas collected at 14 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. 

CITATION. When using this data please cite this dataset deposit and the associated paper where the dataset and baselines are explained in detail: "PanAf20K: A Large Video Dataset for Wild Ape Detection and Behaviour Recognition" published at the International Journal of Computer Vision (IJCV) available here https://doi.org/10.1007/s11263-024-02003-z . For BIBTEX citation details please see the project website at https://obrookes.github.io/panaf.github.io

ACKNOWLEDGEMENTS. We thank the Pan African Programme: 'The Cultured Chimpanzee' team and its collaborators for allowing the use of their data for this paper. We thank Amelie Pettrich, Antonio Buzharevski, Eva Martinez Garcia, Ivana Kirchmair, Sebastian Schütte, Linda Gerlach and Fabina Haas. We also thank management and support staff across all sites; specifically Yasmin Moebius, Geoffrey Muhanguzi, Martha Robbins, Henk Eshuis, Sergio Marrocoli and John Hart. Thanks to the team at https://www.chimpandsee.org particularly Briana Harder, Anja Landsmann, Laura K. Lynn, Zuzana Macháčková, Heidi Pfund, Kristeena Sigler and Jane Widness. The work that allowed for the collection of the dataset was funded by the Max Planck Society, Max Planck Society Innovation Fund, and Heinz L. Krekeler. In this respect we would like to thank: Ministre des Eaux et Forêts, Ministère de l'Enseignement supérieur et de la Recherche scientifique in Côte d'Ivoire; Institut Congolais pour la Conservation de la Nature, Ministère de la Recherche scientifique in Democratic Republic of Congo; Forestry Development Authority in Liberia; Direction des Eaux et Forêts, Chasses et Conservation des Sols in Senegal; Makerere University Biological Field Station, Uganda National Council for Science and Technology, Uganda Wildlife Authority, National Forestry Authority in Uganda; National Institute for Forestry Development and Protected Area Management, Ministry of Agriculture and Forests, Ministry of Fisheries and Environment in Equatorial Guinea.

WEBSITE. Further materials are available at the project website at https://obrookes.github.io/panaf.github.io