A Large Video Dataset for Wild Ape Detection and Behaviour Recognition
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DESCRIPTION. We present the PanAf20K dataset, at the time of publication 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. CITATION. When using this data please cite this dataset deposit and the associated paper where the dataset and baselines are explained in detail: "PanAf20K: A Large Video Dataset for Wild Ape Detection and Behaviour Recognition" published at the International Journal of Computer Vision (IJCV) available here https://doi.org/10.1007/s11263-024-02003-z . For BIBTEX citation details please see the project website at https://obrookes.github.io/panaf.github.io ACKNOWLEDGEMENTS. We thank the Pan African Programme: 'The Cultured Chimpanzee' team and its collaborators for allowing the use of their data for this paper. We thank Amelie Pettrich, Antonio Buzharevski, Eva Martinez Garcia, Ivana Kirchmair, Sebastian Schütte, Linda Gerlach and Fabina Haas. We also thank management and support staff across all sites; specifically Yasmin Moebius, Geoffrey Muhanguzi, Martha Robbins, Henk Eshuis, Sergio Marrocoli and John Hart. Thanks to the team at https://www.chimpandsee.org particularly Briana Harder, Anja Landsmann, Laura K. Lynn, Zuzana Macháčková, Heidi Pfund, Kristeena Sigler and Jane Widness. 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. In this respect we would like to thank: Ministre des Eaux et Forêts, Ministère de l'Enseignement supérieur et de la Recherche scientifique in Côte d'Ivoire; Institut Congolais pour la Conservation de la Nature, Ministère de la Recherche scientifique in Democratic Republic of Congo; Forestry Development Authority in Liberia; Direction des Eaux et Forêts, Chasses et Conservation des Sols in Senegal; Makerere University Biological Field Station, Uganda National Council for Science and Technology, Uganda Wildlife Authority, National Forestry Authority in Uganda; National Institute for Forestry Development and Protected Area Management, Ministry of Agriculture and Forests, Ministry of Fisheries and Environment in Equatorial Guinea. WEBSITE. Further materials are available at the project website at https://obrookes.github.io/panaf.github.io
DESCRIPTION. 本研究提出PanAf20K数据集，在发布之时，它是目前规模最大、多样性最丰富的自然生境下类人猿开放获取标注视频数据集。该数据集包含约20000段相机陷阱（camera trap）视频中的超700万帧画面，记录了黑猩猩与大猩猩的活动，这些数据采集自热带非洲14个野外站点，属于“泛非洲计划：文化黑猩猩（Pan African Programme: The Cultured Chimpanzee）”项目的一部分。该影像数据配套了丰富的标注集与基准测试集，可用于训练和测试各类具有挑战性且生态意义重大的计算机视觉任务，包括类人猿检测与行为识别。鉴于国际自然保护联盟（International Union for Conservation of Nature, IUCN）目前已将所有类人猿科物种列为濒危（Endangered）或极危（Critically Endangered）物种，推进对相机陷阱数据的人工智能分析至关重要。我们期望本数据集能够为人工智能领域研究者参与相关研究提供坚实基础，以提升模型性能、运算效率与结果解读能力，从而助力类人猿种群存在量、丰度、分布及行为的评估工作，最终服务于物种保护实践。 CITATION. 使用本数据集时，请引用本数据集存档文件与详细阐释数据集及基准测试方案的相关论文：发表于国际计算机视觉杂志（International Journal of Computer Vision, IJCV）的"PanAf20K：面向野生类人猿检测与行为识别的大型视频数据集"，论文链接为https://doi.org/10.1007/s11263-024-02003-z。如需获取BIBTEX格式引用信息，请访问项目官网https://obrookes.github.io/panaf.github.io。 ACKNOWLEDGEMENTS. 感谢“泛非洲计划：文化黑猩猩”项目团队及其合作者允许我们在本研究中使用其数据。感谢Amelie Pettrich、Antonio Buzharevski、Eva Martinez Garcia、Ivana Kirchmair、Sebastian Schütte、Linda Gerlach与Fabina Haas。同时感谢所有野外站点的管理与支持人员，具体包括Yasmin Moebius、Geoffrey Muhanguzi、Martha Robbins、Henk Eshuis、Sergio Marrocoli及John Hart。感谢https://www.chimpandsee.org团队，尤其是Briana Harder、Anja Landsmann、Laura K. Lynn、Zuzana Macháčková、Heidi Pfund、Kristeena Sigler与Jane Widness。本数据集的采集工作得到了马克斯·普朗克学会（Max Planck Society）、马克斯·普朗克学会创新基金及Heinz L. Krekeler的资助。在此，我们谨致谢忱：科特迪瓦水利与森林部、科特迪瓦高等教育与科研部；刚果自然保护研究所、刚果民主共和国科研部；利比里亚林业发展局；塞内加尔水利、森林、狩猎与土壤保护局；乌干达马克雷雷大学生物野外站、乌干达国家科学技术委员会、乌干达野生动物管理局、乌干达国家林业局；赤道几内亚林业发展与保护区管理国家研究所、农业与林业部、渔业与环境部。 WEBSITE. 更多相关资料可通过项目官网https://obrookes.github.io/panaf.github.io获取。
