Abstract
Accurate recognition and re-identification of individual animals is essential for successful wildlife population monitoring. Traditional methods, such as leg banding of birds, are time consuming and invasive. Recent progress in artificial intelligence, particularly computer vision, offers encouraging solutions for smart conservation and efficient automation. This study presents a unique pipeline for extracting high-quality key frames from videos of kÄkÄ (Nestor meridionalis), a threatened forest-dwelling parrot in New Zealand. Key frame extraction is well-studied in person re-identification, however, its application to wildlife is limited. Using video recordings at a custom-built feeder, we extract key frames and evaluate the re-identification performance of our pipeline. Our unsupervised methodology combines object detection using YOLO and Grounding DINO, optical flow blur detection, image encoding with DINOv2, and clustering methods to identify representative key frames. The results indicate that our proposed key frame selection methods yield image collections which achieve high accuracy in kÄkÄ re-identification, providing a foundation for future research using media collected in more diverse and challenging environments. Through the use of artificial intelligence and computer vision, our non-invasive and efficient approach provides a valuable alternative to traditional physical tagging methods for recognising kÄkÄ individuals and therefore improving the monitoring of populations. This research contributes to developing fresh approaches in wildlife monitoring, with applications in ecology and conservation biology.
Abstract (translated)
准确识别和重新识别野生动物个体对于成功监测野生动植物种群至关重要。传统方法，例如给鸟类佩戴脚环，既耗时又具有侵扰性。近年来，人工智能（特别是计算机视觉）的进步为智能保护和高效自动化提供了令人鼓舞的解决方案。本研究提出了一套独特的管道系统，用于从新西兰特有濒危森林鹦鹉——kākā (Nestor meridionalis) 的视频中提取高质量的关键帧。关键帧提取在人员重识别领域已得到充分研究，但在野生动物应用中的使用还相对有限。 我们通过定制设计的喂食器记录下的视频来提取这些关键帧，并评估该管道系统的重新识别性能。我们的无监督方法结合了YOLO和Grounding DINO进行物体检测、基于光流模糊检测的方法以及利用DINOv2进行图像编码，最后采用聚类技术以确定代表性的关键帧。 实验结果显示，我们提出的选帧方法能够生成高精度的用于kākā重新识别的图像集，在更多样化且更具挑战性的环境中采集媒体的基础上奠定了未来研究的基础。通过使用人工智能和计算机视觉，我们的非侵入性和高效的方法为传统的物理标记方法提供了一个有价值的替代方案，以更有效地识别个体 kākā 并改进种群监测。 这项研究有助于开发新的野生动物监测方法，在生态学和保护生物学领域具有广泛的应用价值。
URL
https://arxiv.org/abs/2510.08775
