Department of Agroforesty and Landscape, University of Padua, 35020 Legnaro, Italy.
College of Land Science and Technology, China Agricultural University, Beijing 100083, China.
Sensors (Basel). 2018 Oct 24;18(11):3603. doi: 10.3390/s18113603.
Information on the body shape of pigs is a key indicator to monitor their performance and health and to control or predict their market weight. Manual measurements are among the most common ways to obtain an indication of animal growth. However, this approach is laborious and difficult, and it may be stressful for both the pigs and the stockman. The present paper proposes the implementation of a Structure from Motion (SfM) photogrammetry approach as a new tool for on-barn animal reconstruction applications. This is possible also to new software tools allowing automatic estimation of camera parameters during the reconstruction process even without a preliminary calibration phase. An analysis on pig body 3D SfM characterization is here proposed, carried out under different conditions in terms of number of camera poses and animal movements. The work takes advantage of the total reconstructed surface as reference index to quantify the quality of the achieved 3D reconstruction, showing how as much as 80% of the total animal area can be characterized.
猪体形状的信息是监测其性能和健康状况以及控制或预测其市场体重的关键指标。手动测量是获得动物生长指示的最常用方法之一。然而，这种方法既繁琐又困难，而且对猪和饲养员来说都可能有压力。本文提出了一种运动结构（SfM）摄影测量方法的实现，作为一种新的工具，用于在棚内进行动物重建应用。这也是可能的，因为新的软件工具允许在重建过程中自动估计相机参数，即使没有初步的校准阶段。这里提出了一种关于猪体 3D SfM 特征化的分析，在不同的相机姿态数量和动物运动条件下进行。该工作利用总重建表面作为参考指标来量化所达到的 3D 重建的质量，结果表明，多达 80%的动物总面积可以得到描述。
