PanAf20K: A Large Video Dataset for Wild Ape Detection and Behaviour Recognition

Research output: Contribution to journalArticle (Academic Journal)peer-review


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 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. The dataset and code are available from the project website
Original languageEnglish
JournalInternational Journal of Computer Vision (IJCV)
Early online date4 Mar 2024
Publication statusE-pub ahead of print - 4 Mar 2024

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© The Author(s) 2024.


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