Wang Yueming, Chen Tiantian, Li Baoshan, Li Qi
School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou 014010, China.
J Anim Sci Technol. 2023 May;65(3):519-534. doi: 10.5187/jast.2022.e87. Epub 2023 May 31.
Rumination in cattle is closely related to their health, which makes the automatic monitoring of rumination an important part of smart pasture operations. However, manual monitoring of cattle rumination is laborious and wearable sensors are often harmful to animals. Thus, we propose a computer vision-based method to automatically identify multi-object cattle rumination, and to calculate the rumination time and number of chews for each cow. The heads of the cattle in the video were initially tracked with a multi-object tracking algorithm, which combined the You Only Look Once (YOLO) algorithm with the kernelized correlation filter (KCF). Images of the head of each cow were saved at a fixed size, and numbered. Then, a rumination recognition algorithm was constructed with parameters obtained using the frame difference method, and rumination time and number of chews were calculated. The rumination recognition algorithm was used to analyze the head image of each cow to automatically detect multi-object cattle rumination. To verify the feasibility of this method, the algorithm was tested on multi-object cattle rumination videos, and the results were compared with the results produced by human observation. The experimental results showed that the average error in rumination time was 5.902% and the average error in the number of chews was 8.126%. The rumination identification and calculation of rumination information only need to be performed by computers automatically with no manual intervention. It could provide a new contactless rumination identification method for multi-cattle, which provided technical support for smart pasture.
牛的反刍与它们的健康密切相关，这使得反刍的自动监测成为智能牧场运营的重要组成部分。然而，人工监测牛的反刍劳动强度大，且可穿戴传感器往往对动物有害。因此，我们提出一种基于计算机视觉的方法来自动识别多目标牛的反刍行为，并计算每头牛的反刍时间和咀嚼次数。视频中牛的头部最初使用一种多目标跟踪算法进行跟踪，该算法将你只看一次（YOLO）算法与核相关滤波器（KCF）相结合。每头牛头部的图像以固定尺寸保存并编号。然后，利用帧差法获得的参数构建反刍识别算法，并计算反刍时间和咀嚼次数。反刍识别算法用于分析每头牛的头部图像，以自动检测多目标牛的反刍行为。为验证该方法的可行性，该算法在多目标牛反刍视频上进行测试，并将结果与人工观察结果进行比较。实验结果表明，反刍时间的平均误差为5.902%，咀嚼次数的平均误差为8.126%。反刍行为的识别和反刍信息的计算仅需计算机自动执行，无需人工干预。它可以为多头牛提供一种新的非接触式反刍识别方法，为智能牧场提供技术支持。
