Abstract
Precision livestock farming requires objective assessment of social behavior to support herd welfare monitoring, yet most existing approaches infer interactions using static proximity thresholds that cannot distinguish affiliative from agonistic behaviors in complex barn environments. This limitation constrains the interpretability of automated social network analysis in commercial settings. We present a pose-based computational framework for interaction classification that moves beyond proximity heuristics by modeling the spatiotemporal geometry of anatomical keypoints. Rather than relying on pixel-level appearance or simple distance measures, the proposed method encodes interaction-specific motion signatures from keypoint trajectories, enabling differentiation of social interaction valence. The framework is implemented as an end-to-end computer vision pipeline integrating YOLOv11 for object detection (mAP@0.50: 96.24%), supervised individual identification (98.24% accuracy), ByteTrack for multi-object tracking (81.96% accuracy), ZebraPose for 27-point anatomical keypoint estimation, and a support vector machine classifier trained on pose-derived distance dynamics. On annotated interaction clips collected from a commercial dairy barn, the classifier achieved 77.51% accuracy in distinguishing affiliative and agonistic behaviors using pose information alone. Comparative evaluation against a proximity-only baseline shows substantial gains in behavioral discrimination, particularly for affiliative interactions. The results establish a proof-of-concept for automated, vision-based inference of social interactions suitable for constructing interaction-aware social networks, with near-real-time performance on commodity hardware.
Abstract (translated)
精准畜牧业需要对社会行为进行客观评估，以支持群福利监测。然而，大多数现有方法通过使用静态接近阈值来推断互动关系，这在复杂的农场环境中无法区分亲和与对抗性行为。这种限制制约了自动化社交网络分析在商业环境中的解释能力。 我们提出了一种基于姿态的计算框架，用于分类交互行为，该框架超越了基于接近度的经验法则，通过建模解剖关键点的空间时间几何结构来进行互动识别。我们的方法不依赖于像素级别的外观或简单的距离测量，而是从关键点轨迹中编码特定于交互的动作签名，从而能够区分社交互动的性质。 此框架被实现为一个端到端计算机视觉流水线，包括YOLOv11对象检测（mAP@0.5: 96.24%），监督下的个体识别（98.24%准确性），ByteTrack多目标跟踪（81.96%的准确率），ZebraPose进行27点解剖关键点估计，以及一个支持向量机分类器，该分类器基于姿态衍生的距离动态性训练。在从商业乳牛场收集的注释互动剪辑上，仅使用姿态信息，分类器在区分亲和与对抗行为方面达到了77.51%的准确性。 对比只依赖接近度的方法，我们的方法在行为识别上的表现有显著提高，特别是在亲和行为上。这些结果为基于视觉自动化地推断社交互动的概念验证提供了依据，并且能够在普通硬件上实现近实时性能以构建了解交互的社交网络。
URL
https://arxiv.org/abs/2512.14998
