Abstract Body condition scoring of livestock is widely used as a subjective method for assessing energy reserves and making management decisions for livestock. Since animal dimensions are often measured manually, the procedure is time-consuming, expensive and stressful for both the farmer and animal. Recent advances in three-dimensional sensor technology provide innovative tools for the design of automated contactless systems for assessing the animal body condition. The objective of this paper is to design an automated computer vision system capable to generate an accurate three-dimensional model of live cattle. The system is based on a non-rigid 3-D shape reconstruction utilizing data from depth cameras. The design methodology includes three Microsoft Kinect v2 cameras, computer vision, signal filtering of point clouds, pattern recognition using 3-D feature extraction techniques, and statistical analysis using point and interval estimations. The quality of generated three-dimensional body models is validated against manually measured nine references, such as withers height, hip height, chest depth, oblique body length, heart girth, etc. With a 90% confidence level, measurement errors in the proposed system among all measured estimates are less than 3%. Experimental results show that the proposed approach can serve as a new accurate method for non-contact body measurement of livestock.

中文翻译：

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使用三个深度相机和非刚性 3-D 形状恢复对活牛进行准确的身体测量

摘要 牲畜体况评分作为评估牲畜能量储备和制定管理决策的主观方法被广泛使用。由于动物尺寸通常是手动测量的，因此该过程既费时又费钱，而且对农场主和动物来说都是压力山大。三维传感器技术的最新进展为设计用于评估动物身体状况的自动非接触式系统提供了创新工具。本文的目的是设计一种自动化计算机视觉系统，能够生成准确的活牛 3D 模型。该系统基于利用深度相机数据的非刚性 3-D 形状重建。设计方法包括三个 Microsoft Kinect v2 摄像头、计算机视觉、点云信号过滤、使用 3-D 特征提取技术的模式识别，以及使用点和区间估计的统计分析。生成的三维身体模型的质量根据手动测量的九个参考进行验证，例如马肩隆高度、臀部高度、胸深、斜体长度、心脏周长等。 在 90% 的置信水平下，所提出的系统中的测量误差在所有测量的估计值中都小于 3%。实验结果表明，所提出的方法可以作为一种新的非接触式牲畜身体测量的准确方法。心脏周长等。在 90% 的置信水平下，建议系统中所有测量估计值的测量误差小于 3%。实验结果表明，所提出的方法可以作为一种新的非接触式牲畜身体测量的准确方法。心脏周长等。在 90% 的置信水平下，建议系统中所有测量估计值的测量误差小于 3%。实验结果表明，所提出的方法可以作为一种新的非接触式牲畜身体测量的准确方法。