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Visual WetlandBirds Dataset: Bird Species Identification and Behavior Recognition in Videos

Javier Rodriguez-Juan, David Ortiz-Perez, Manuel Benavent-Lledo, David Mulero-Pérez, Pablo Ruiz-Ponce, Adrian Orihuela-Torres, Jose Garcia-Rodriguez, Esther Sebastián-González

TL;DR

This study introduces the first fine-grained video dataset specifically designed for bird behavior detection and species classification, and presents baseline results using state of the art models on two tasks: bird behavior recognition and species classification.

Abstract

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.

Visual WetlandBirds Dataset: Bird Species Identification and Behavior Recognition in Videos

TL;DR

This study introduces the first fine-grained video dataset specifically designed for bird behavior detection and species classification, and presents baseline results using state of the art models on two tasks: bird behavior recognition and species classification.

Abstract

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.
Paper Structure (13 sections, 5 figures, 4 tables)

This paper contains 13 sections, 5 figures, 4 tables.

Table of Contents

  1. Background & Summary
  2. Methods
  3. Data Records
  4. Technical Validation
  5. Usage Notes
  6. Code availability
  7. Acknowledgements
  8. Author contributions statement
  9. Competing interests

Figures (5)

  • Figure 1: Comparison of total number of clips per behavior and mean duration of behavior clips.
  • Figure 2: Frame samples where gregarious birds appear performing different behaviors.
  • Figure 3: Video frame crops of bird species performing the 7 behaviors composing the dataset.
  • Figure 4: Visual representation of stages involved in the annotation process. Birds are first classified into species by annotators and localized using a YOLO model, then annotators recognize bird behaviors and subjects are identified using a Python script, and finally the data is curated and post-processed.
  • Figure 5: Confusion matrix of species classification pipeline.