Early and non-intrusive lameness detection in dairy cows using 3-dimensional video
Early and non-intrusive lameness detection in dairy cows using 3-dimensional video
复制标题
DOI：
10.1016/j.biosystemseng.2016.09.017
复制
发表时间：
2017-01-01
影响因子：
5.1
通讯作者：
Smith, Lyndon N.
中科院分区：
文献类型：
--
作者：
Jabbar, K. Abdul;Hansen, Mark F.;Smith, Lyndon N.
跛行是奶牛群的主要问题，其早期和自动化检测提供了动物福利效益，并为农民提供了潜在的高商业节省。目前在自动检测方面的进展还没有达到对早期跛行分类的敏感措施。介绍了一种利用三维深度视频数据分析动物步态不对称性的新型跛行代理方法。这一动态代理来源于行走时髋关节的高度变化。视频捕捉设置是完全隐蔽的，它有利于自动化过程。这些动物在挤奶后自由地排成一列，用头顶的3D深度摄像机记录下来。牛身体的3D深度图像用于自动跟踪关键区域，如钩子和脊柱。从这些区域计算高度运动，形成本研究的运动信号，并使用希尔伯特变换对其进行分析。利用1-5运动评分（LS）系统对22头荷斯坦奶牛进行测试，结果表明，LS 1和LS 2（及以上）之间存在一个阈值。这个界限很重要，因为它代表了被认为是跛牛的最早时间点，它的早期发现可以改善干预结果，从而最大限度地减少损失并减少动物的痛苦。采用线性支持向量机（SVM）二值分类模型，阈值准确率达到95.7%，灵敏度为100%（检测跛牛），特异性为75%（检测非跛牛）。(c) 2016 i同意。Elsevier Ltd.出版。版权所有。
Lameness is a major issue in dairy herds and its early and automated detection offers animal welfare benefits together with potentially high commercial savings for farmers. Current advancements in automated detection have not achieved a sensitive measure for classifying early lameness. A novel proxy for lameness using 3-dimensional (3D) depth video data to analyse the animal's gait asymmetry is introduced. This dynamic proxy is derived from the height variations in the hip joint during walking. The video capture setup is completely covert and it facilitates an automated process. The animals are recorded using an overhead 3D depth camera as they walk freely in single file after the milking session. A 3D depth image of the cow's body is used to automatically track key regions such as the hooks and the spine. The height movements are calculated from these regions to form the locomotion signals of this study, which are analysed using a Hilbert transform. Our results using a 1-5 locomotion scoring (LS) system on 22 Holstein Friesian dairy cows, a threshold could be identified between LS 1 and 2 (and above). This boundary is important as it represents the earliest point in time at which a cow is considered lame, and its early detection could improve intervention outcome thereby minimising losses and reducing animal suffering. Using a linear Support Vector Machine (SVM) binary classification model, the threshold achieved an accuracy of 95.7% with a 100% sensitivity (detecting lame cows) and 75% specificity (detecting non-lame cows). (c) 2016 IAgrE. Published by Elsevier Ltd. All rights reserved.