用无人机视频自动追踪大象姿态，助力群体行为研究
Whole-Herd Elephant Pose Estimation from Drone Data for Collective Behavior Analysis
- 基于无人机视频，用两种姿态估计模型分析大象群体动作
- YOLO-NAS-Pose在姿态和检测任务上均优于DeepLabCut
- 适合野生动物监测与保护领域的研究人员参考
本研究首次将自动化姿态估计应用于无人机拍摄的野外象群数据，基于肯尼亚萨姆布鲁国家保护区的视频，评估了DeepLabCut与新型YOLO-NAS-Pose两种方法。模型针对低分辨率（约50像素）大象进行训练，识别头部、脊柱、耳朵等关键点。测试集上，两种方法均表现良好，可支持基础行为分析。在根均方误差（RMSE）、正确关键点率（PCK）和物体关键点相似度（OKS）指标上，YOLO-NAS-Pose优于DeepLabCut；且在目标检测任务中也表现更优。该方法为野生动物行为研究，尤其是无人机监测领域提供了新范式，具有重要保护应用价值。
原文摘要 · Abstract (English)
This research represents a pioneering application of automated pose estimation from drone data to study elephant behavior in the wild, utilizing video footage captured from Samburu National Reserve, Kenya. The study evaluates two pose estimation workflows: DeepLabCut, known for its application in laboratory settings and emerging wildlife fieldwork, and YOLO-NAS-Pose, a newly released pose estimation model not previously applied to wildlife behavioral studies. These models are trained to analyze elephant herd behavior, focusing on low-resolution ($\sim$50 pixels) subjects to detect key points such as the head, spine, and ears of multiple elephants within a frame. Both workflows demonstrated acceptable quality of pose estimation on the test set, facilitating the automated detection of basic behaviors crucial for studying elephant herd dynamics. For the metrics selected for pose estimation evaluation on the test set -- root mean square error (RMSE), percentage of correct keypoints (PCK), and object keypoint similarity (OKS) -- the YOLO-NAS-Pose workflow outperformed DeepLabCut. Additionally, YOLO-NAS-Pose exceeded DeepLabCut in object detection evaluation. This approach introduces a novel method for wildlife behavioral research, including the burgeoning field of wildlife drone monitoring, with significant implications for wildlife conservation.
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