CHIRP dataset: towards long-term, individual-level, behavioral monitoring of bird populations in the wild

2026-03-26

AI Summary

This study addresses the lack of multi-task computer vision datasets for long-term behavioral monitoring of individual animals in the wild, a gap that has hindered the advancement of automated behavioral analysis in biodiversity research. To bridge this gap, the authors introduce the CHIRP dataset, which uniquely integrates annotations for multiple tasks—including re-identification, action recognition, 2D keypoint estimation, object detection, and instance segmentation—for the first time. They further propose CORVID, a novel paradigm for individual re-identification based on probabilistic matching of colored leg bands. By incorporating biologically meaningful evaluation metrics such as feeding rate and co-occurrence rate, CORVID significantly outperforms existing methods in real-world ecological settings, effectively narrowing the divide between computer vision techniques and ecological applications.

Technology Category

• Computer Vision: Multi-modal Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Humans and AI: Crowd Sourcing and Human Computation

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Security and Privacy: Data transparency and provenance

Abstract

Long-term behavioral monitoring of individual animals is crucial for studying behavioral changes that occur over different time scales, especially for conservation and evolutionary biology. Computer vision methods have proven to benefit biodiversity monitoring, but automated behavior monitoring in wild populations remains challenging. This stems from the lack of datasets that cover a range of computer vision tasks necessary to extract biologically meaningful measurements of individual animals. Here, we introduce such a dataset (CHIRP) with a new method (CORVID) for individual re-identification of wild birds. The CHIRP (Combining beHaviour, Individual Re-identification and Postures) dataset is curated from a long-term population of wild Siberian jays studied in Swedish Lapland, supporting re-identification (re-id), action recognition, 2D keypoint estimation, object detection, and instance segmentation. In addition to traditional task-specific benchmarking, we introduce application-specific benchmarking with biologically relevant metrics (feeding rates, co-occurrence rates) to evaluate the performance of models in real-world use cases. Finally, we present CORVID (COlouR-based Video re-ID), a novel pipeline for individual identification of birds based on the segmentation and classification of colored leg rings, a widespread approach for visual identification of individual birds. CORVID offers a probability-based id tracking method by matching the detected combination of color rings with a database. We use application-specific benchmarking to show that CORVID outperforms state-of-the-art re-id methods. We hope this work offers the community a blueprint for curating real-world datasets from ethically approved biological studies to bridge the gap between computer vision research and biological applications.

Problem

• long-term monitoring
• individual-level behavior
• wild bird populations
• computer vision dataset
• behavioral monitoring

Innovation

• individual re-identification
• behavioral monitoring
• computer vision
• wildlife dataset
• color-based tracking