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.
Abstract (translated)
对个体动物进行长期行为监测对于研究不同时间尺度上发生的行为变化至关重要，尤其在保护生物学和进化生物学领域。计算机视觉方法已被证明有益于生物多样性监测，但在野生种群中实现自动化行为监测仍具挑战性。这源于缺乏涵盖多种计算机视觉任务的数据集，而这些任务是提取个体动物具有生物学意义的测量指标所必需的。在此，我们推出这样一个数据集（CHIRP）以及一种针对野生鸟类个体重识别的新方法（CORVID）。CHIRP（结合行为、个体重识别与姿态）数据集源自瑞典拉普兰地区对野生西伯兰松鸦的长期种群研究，支持重识别（re-id）、动作识别、2D关键点估计、目标检测和实例分割任务。除传统的任务特定基准测试外，我们还引入了基于生物学相关指标（取食率、共存率）的应用特定基准测试，以评估模型在真实场景中的性能。最后，我们提出了CORVID（基于颜色的视频重识别），这是一种基于彩色腿环分割与分类的新型鸟类个体识别流程——彩色腿环是鸟类视觉个体识别的常用方法。CORVID通过将检测到的彩色腿环组合与数据库匹配，提供基于概率的个体追踪方法。我们利用应用特定基准测试表明，CORVID优于最先进的重识别方法。我们希望本研究能为学界提供一个从伦理审批的生物研究中策划真实世界数据集的蓝图，以弥合计算机视觉研究与生物学应用之间的差距。
URL
https://arxiv.org/abs/2603.25524
