The PanAf-FGBG Dataset The PanAf Foreground-Background Dataset

DESCRIPTION. The PanAf-FGBG dataset comprises behaviour-annotated video footage of wild chimpanzees from more than 350 camera locations across tropical Africa, collected by the Pan African Programme: The Cultured Chimpanzee. It includes paired foreground (with chimpanzees) and background (without chimpanzees) videos, allowing controlled analysis of background influence on behaviour recognition models. The dataset is split into overlapping and disjoint camera location views to support evaluation under both in-distribution and out-of-distribution conditions. Each entry is accompanied by metadata and multi-label annotations for 14 distinct behaviours, enabling robust model training and testing. This resource aims to enhance AI models for wildlife behaviour understanding and supports broader conservation efforts for endangered great ape species. CITATION. When using this data please cite this dataset deposit and the associated paper where the dataset and baselines are explained in detail: "The PanAf-FGBG Dataset: Understanding the Impact of Backgrounds in Wildlife Behaviour Recognition" published in the 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) available here: openaccess.thecvf.com. For BIBTEX citation details please see the project website: obrookes.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 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. This work was supported by the UKRI CDT in Interactive AI (grant EP/S022937/1). This work was in part supported by the US National Science Foundation Awards No. 2118240 "HDR Institute: Imageomics: A New Frontier of Biological Information Powered by Knowledge-Guided Machine Learning" and Award No. 2330423 and Natural Sciences and Engineering Research Council of Canada under Award No. 585136 for the "AI and Biodiversity Change (ABC) Global Center". WEBSITE. Further materials are available at the project website at: obrookes.github.io