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.

本研究解决了现有鸟类视频数据集稀缺且缺乏详细行为注释的问题。我们提出了一个首个细粒度视频数据集，专为鸟类行为检测和物种分类设计，包含178段在西班牙湿地录制的视频，涵盖13种鸟类和7种不同的行为类别。这一数据集将促进深度学习模型的发展，推动鸟类行为识别和物种分类的研究。

视觉湿地鸟类数据集：鸟类物种识别与行为识别的视频研究