TY - JOUR
T1 - PanAf20K
T2 - a large video dataset for wild ape detection and behaviour recognition
AU - Brookes, Otto
AU - Mirmehdi, Majid
AU - Stephens, Colleen
AU - Angedakin, Samuel
AU - Corogenes, Katherine
AU - Dowd, Dervla
AU - Dieguez, Paula
AU - Hicks, Thurston C.
AU - Jones, Sorrel
AU - Lee, Kevin
AU - Leinert, Vera
AU - Lapuente, Juan
AU - McCarthy, Maureen S.
AU - Meier, Amelia
AU - Murai, Mizuki
AU - Normand, Emmanuelle
AU - Vergnes, Virginie
AU - Wessling, Erin G.
AU - Wittig, Roman M.
AU - Langergraber, Kevin
AU - Maldonado, Nuria
AU - Yang, Xinyu
AU - Zuberbühler, Klaus
AU - Boesch, Christophe
AU - Arandjelovic, Mimi
AU - Kühl, Hjalmar
AU - Burghardt, Tilo
N1 - 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.
This work was supported by the UKRI CDT in Interactive AI under grant EP/S022937/1.
PY - 2024/3/4
Y1 - 2024/3/4
N2 - 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
AB - 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
KW - Animal biometrics
KW - Video dataset
KW - Behaviour recognition
KW - Wildlife Imageomics
KW - Conservation technology
U2 - 10.1007/s11263-024-02003-z
DO - 10.1007/s11263-024-02003-z
M3 - Article
SN - 1573-1405
JO - International Journal of Computer Vision
JF - International Journal of Computer Vision
ER -