PanAf20K: a large video dataset for wild ape detection and behaviour recognition

Otto Brookes*, Majid Mirmehdi, Colleen Stephens, Samuel Angedakin, Katherine Corogenes, Dervla Dowd, Paula Dieguez, Thurston C. Hicks, Sorrel Jones, Kevin Lee, Vera Leinert, Juan Lapuente, Maureen S. McCarthy, Amelia Meier, Mizuki Murai, Emmanuelle Normand, Virginie Vergnes, Erin G. Wessling, Roman M. Wittig, Kevin LangergraberNuria Maldonado, Xinyu Yang, Klaus Zuberbühler, Christophe Boesch, Mimi Arandjelovic, Hjalmar Kühl, Tilo Burghardt

*Corresponding author for this work

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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
Original languageEnglish
Number of pages17
JournalInternational Journal of Computer Vision
Publication statusPublished - 4 Mar 2024


  • Animal biometrics
  • Video dataset
  • Behaviour recognition
  • Wildlife Imageomics
  • Conservation technology


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