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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2501.08931 |
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| _version_ | 1866917893055184896 |
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| author | Rodriguez-Juan, Javier Ortiz-Perez, David Benavent-Lledo, Manuel Mulero-Pérez, David Ruiz-Ponce, Pablo Orihuela-Torres, Adrian Garcia-Rodriguez, Jose Sebastián-González, Esther |
| author_facet | Rodriguez-Juan, Javier Ortiz-Perez, David Benavent-Lledo, Manuel Mulero-Pérez, David Ruiz-Ponce, Pablo Orihuela-Torres, Adrian Garcia-Rodriguez, Jose Sebastián-González, Esther |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_08931 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Visual WetlandBirds Dataset: Bird Species Identification and Behavior Recognition in Videos Rodriguez-Juan, Javier Ortiz-Perez, David Benavent-Lledo, Manuel Mulero-Pérez, David Ruiz-Ponce, Pablo Orihuela-Torres, Adrian Garcia-Rodriguez, Jose Sebastián-González, Esther Computer Vision and Pattern Recognition Artificial Intelligence 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. |
| title | Visual WetlandBirds Dataset: Bird Species Identification and Behavior Recognition in Videos |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2501.08931 |