Rodriguez-Juan Javier, Ortiz-Perez David, Benavent-Lledo Manuel, Mulero-Pérez David, Ruiz-Ponce Pablo, Orihuela-Torres Adrian, Garcia-Rodriguez Jose, Sebastián-González Esther
Department of Computer Technology, University of Alicante, Alicante, 03690, Spain.
Department of Ecology, University of Alicante, Alicante, 03690, Spain.
Sci Data. 2025 Jul 11;12(1):1200. doi: 10.1038/s41597-025-05516-5.
The current biodiversity loss crisis makes animal monitoring a relevant field of study. In light of this, data collected through monitoring can provide essential insights, and information for decision-making aimed at preserving global biodiversity. Despite the importance of such data, there is a notable scarcity of datasets featuring videos of birds, and none of the existing datasets offer detailed annotations of bird behaviors in video format. In response to this gap, our study introduces the first fine-grained video dataset specifically designed for bird behavior detection and species classification. This dataset addresses the need for comprehensive bird video datasets and provides detailed data on bird actions, facilitating the development of deep learning models to recognize these, similar to the advancements made in human action recognition. The proposed dataset comprises 178 videos recorded in Spanish wetlands, capturing 13 different bird species performing 7 distinct behavior classes. In addition, we also present baseline results using state of the art models on two tasks: bird behavior recognition and species classification.
当前的生物多样性丧失危机使动物监测成为一个相关的研究领域。有鉴于此，通过监测收集的数据可以提供重要的见解，以及用于旨在保护全球生物多样性的决策的信息。尽管此类数据很重要，但以鸟类视频为特色的数据集明显稀缺，并且现有的数据集中没有一个以视频格式提供鸟类行为的详细注释。为了弥补这一差距，我们的研究引入了第一个专门为鸟类行为检测和物种分类设计的细粒度视频数据集。该数据集满足了对全面鸟类视频数据集的需求，并提供了有关鸟类行为的详细数据，促进了深度学习模型的开发以识别这些行为，类似于人类动作识别方面取得的进展。所提出的数据集包括在西班牙湿地录制的178个视频，捕捉了13种不同鸟类物种执行7种不同的行为类别。此外，我们还展示了使用最先进模型在两项任务上的基线结果：鸟类行为识别和物种分类。
