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Journal Article

PanAf20K: A large video dataset for wild ape detection and behaviour recognition (advance online)

MPS-Authors
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Angedakin,  Samuel       
Department of Primatology, Max Planck Institute for Evolutionary Anthropology, Max Planck Society;

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Corogenes,  Katherine
Great Ape Evolutionary Ecology and Conservation, Department of Primatology, Max Planck Institute for Evolutionary Anthropology, Max Planck Society;

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Dieguez,  Paula       
Department of Primatology, Max Planck Institute for Evolutionary Anthropology, Max Planck Society;

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Jones,  Sorrel
Great Ape Evolutionary Ecology and Conservation, Department of Primatology, Max Planck Institute for Evolutionary Anthropology, Max Planck Society;

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Lee,  Kevin
Great Ape Evolutionary Ecology and Conservation, Department of Primatology, Max Planck Institute for Evolutionary Anthropology, Max Planck Society;

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Leinert,  Vera
Department of Primatology, Max Planck Institute for Evolutionary Anthropology, Max Planck Society;

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Lapuente,  Juan
Great Ape Evolutionary Ecology and Conservation, Department of Primatology, Max Planck Institute for Evolutionary Anthropology, Max Planck Society;

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McCarthy,  Maureen       
Great Ape Evolutionary Ecology and Conservation, Department of Primatology, Max Planck Institute for Evolutionary Anthropology, Max Planck Society;

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Meier,  Amelia
Department of Primatology, Max Planck Institute for Evolutionary Anthropology, Max Planck Society;

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Murai,  Mizuki       
Department of Primatology, Max Planck Institute for Evolutionary Anthropology, Max Planck Society;

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Wittig,  Roman M.       
Chimpanzees, Department of Primatology, Max Planck Institute for Evolutionary Anthropology, Max Planck Society;

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Maldonado,  Nuria
Department of Primatology, Max Planck Institute for Evolutionary Anthropology, Max Planck Society;

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Boesch,  Christophe       
Great Ape Evolutionary Ecology and Conservation, Department of Primatology, Max Planck Institute for Evolutionary Anthropology, Max Planck Society;
Chimpanzees, Department of Primatology, Max Planck Institute for Evolutionary Anthropology, Max Planck Society;

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Arandjelovic,  Mimi       
Great Ape Evolutionary Ecology and Conservation, Department of Primatology, Max Planck Institute for Evolutionary Anthropology, Max Planck Society;
Chimpanzees, Department of Primatology, Max Planck Institute for Evolutionary Anthropology, Max Planck Society;

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Kühl,  Hjalmar S.       
Chimpanzees, Department of Primatology, Max Planck Institute for Evolutionary Anthropology, Max Planck Society;

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Brookes_PanAf20K_IntJCompVis_2024.pdf
(Publisher version), 10MB

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Citation

Brookes, O., Mirmehdi, M., Stephens, C., Angedakin, S., Corogenes, K., Dowd, D., et al. (2024). PanAf20K: A large video dataset for wild ape detection and behaviour recognition (advance online). International Journal of Computer Vision. doi:10.1007/s11263-024-02003-z.


Cite as: https://hdl.handle.net/21.11116/0000-000E-B058-8
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 ~ 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. © The Author(s) 2024.