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

2025-01-15

AI Summary

A critical gap exists in ecological monitoring: the absence of field video datasets tailored for fine-grained avian species identification and behavioral analysis. To address this, we introduce WetBird-178—the first fine-grained wetland bird video dataset—comprising 178 high-quality field videos spanning 13 bird species and 7 behavioral categories, with frame-level behavioral annotations. This dataset establishes the first benchmark with spatiotemporally precise behavioral labels for birds, filling a fundamental void in wildlife vision research. We formally define the novel task of fine-grained bird behavior recognition and propose a standardized evaluation protocol. Baseline experiments using state-of-the-art models—including YOLOv8 for species classification and TimeSformer for behavior recognition—demonstrate reproducible performance. WetBird-178 provides an essential data foundation and methodological framework for cross-domain transfer learning, few-shot learning, and intelligent ecological monitoring.

Technology Category

• Computer Vision: Video Understanding & Activity Analysis
• Machine Learning: Time-Series/Data Streams
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Web Mining and Content Analysis: Web data integration and cleaning

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.

Problem

• Bird Behavior
• Species Recognition
• Video Dataset

Innovation

• Bird Species Identification
• Behavior Recognition
• Wetland Bird Dataset