PanAf20K: A Large Video Dataset for Wild Ape Detection and Behaviour Recognition
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| Formato: | Preprint |
| Publicado: |
2024
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| author | Brookes, Otto Mirmehdi, Majid Stephens, Colleen Angedakin, Samuel Corogenes, Katherine Dowd, Dervla Dieguez, Paula Hicks, Thurston C. Jones, Sorrel Lee, Kevin Leinert, Vera Lapuente, Juan McCarthy, Maureen S. Meier, Amelia Murai, Mizuki Normand, Emmanuelle Vergnes, Virginie Wessling, Erin G. Wittig, Roman M. Langergraber, Kevin Maldonado, Nuria Yang, Xinyu Zuberbuhler, Klaus Boesch, Christophe Arandjelovic, Mimi Kuhl, Hjalmar Burghardt, Tilo |
| author_facet | Brookes, Otto Mirmehdi, Majid Stephens, Colleen Angedakin, Samuel Corogenes, Katherine Dowd, Dervla Dieguez, Paula Hicks, Thurston C. Jones, Sorrel Lee, Kevin Leinert, Vera Lapuente, Juan McCarthy, Maureen S. Meier, Amelia Murai, Mizuki Normand, Emmanuelle Vergnes, Virginie Wessling, Erin G. Wittig, Roman M. Langergraber, Kevin Maldonado, Nuria Yang, Xinyu Zuberbuhler, Klaus Boesch, Christophe Arandjelovic, Mimi Kuhl, Hjalmar Burghardt, Tilo |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_13554 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | PanAf20K: A Large Video Dataset for Wild Ape Detection and Behaviour Recognition Brookes, Otto Mirmehdi, Majid Stephens, Colleen Angedakin, Samuel Corogenes, Katherine Dowd, Dervla Dieguez, Paula Hicks, Thurston C. Jones, Sorrel Lee, Kevin Leinert, Vera Lapuente, Juan McCarthy, Maureen S. Meier, Amelia Murai, Mizuki Normand, Emmanuelle Vergnes, Virginie Wessling, Erin G. Wittig, Roman M. Langergraber, Kevin Maldonado, Nuria Yang, Xinyu Zuberbuhler, Klaus Boesch, Christophe Arandjelovic, Mimi Kuhl, Hjalmar Burghardt, Tilo Computer Vision and Pattern Recognition 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. |
| title | PanAf20K: A Large Video Dataset for Wild Ape Detection and Behaviour Recognition |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2401.13554 |