Mon Su Larb, Onizuka Tsubasa, Tin Pyke, Aikawa Masaru, Kobayashi Ikuo, Zin Thi Thi
Graduate School of Engineering, University of Miyazaki, Miyazaki, 889-2192, Japan.
Organization for Learning and Student Development, University of Miyazaki, Miyazaki, 889-2192, Japan.
Sci Rep. 2024 Aug 1;14(1):17779. doi: 10.1038/s41598-024-68418-3.
Video-based monitoring is essential nowadays in cattle farm management systems for automated evaluation of cow health, encompassing body condition scores, lameness detection, calving events, and other factors. In order to efficiently monitor the well-being of each individual animal, it is vital to automatically identify them in real time. Although there are various techniques available for cattle identification, a significant number of them depend on radio frequency or visible ear tags, which are prone to being lost or damaged. This can result in financial difficulties for farmers. Therefore, this paper presents a novel method for tracking and identifying the cattle with an RGB image-based camera. As a first step, to detect the cattle in the video, we employ the YOLOv8 (You Only Look Once) model. The sample data contains the raw video that was recorded with the cameras that were installed at above from the designated lane used by cattle after the milk production process and above from the rotating milking parlor. As a second step, the detected cattle are continuously tracked and assigned unique local IDs. The tracked images of each individual cattle are then stored in individual folders according to their respective IDs, facilitating the identification process. The images of each folder will be the features which are extracted using a feature extractor called VGG (Visual Geometry Group). After feature extraction task, as a final step, the SVM (Support Vector Machine) identifier for cattle identification will be used to get the identified ID of the cattle. The final ID of a cattle is determined based on the maximum identified output ID from the tracked images of that particular animal. The outcomes of this paper will act as proof of the concept for the use of combining VGG features with SVM is an effective and promising approach for an automatic cattle identification system.
如今，基于视频的监测在奶牛场管理系统中对于自动评估奶牛健康状况至关重要，包括体况评分、跛行检测、产犊事件及其他因素。为了有效监测每头个体动物的健康状况，实时自动识别它们至关重要。尽管有多种用于奶牛识别的技术，但其中许多依赖于射频或可见耳标，而这些耳标容易丢失或损坏。这可能给养殖户带来经济困难。因此，本文提出了一种基于RGB图像摄像头跟踪和识别奶牛的新方法。第一步，为了在视频中检测奶牛，我们采用YOLOv8（你只看一次）模型。样本数据包含在挤奶过程后奶牛使用的指定通道上方以及旋转挤奶厅上方安装的摄像头录制的原始视频。第二步，对检测到的奶牛进行持续跟踪并分配唯一的局部ID。然后，每头个体奶牛的跟踪图像根据其各自的ID存储在单独的文件夹中，便于识别过程。每个文件夹中的图像将是使用名为VGG（视觉几何组）的特征提取器提取的特征。在特征提取任务之后，作为最后一步，将使用用于奶牛识别的支持向量机（SVM）标识符来获取奶牛的识别ID。奶牛的最终ID基于该特定动物跟踪图像中最大的识别输出ID来确定。本文的结果将证明将VGG特征与SVM相结合对于自动奶牛识别系统是一种有效且有前景的方法这一概念。
