Cattle farming is one of the important and profitable agricultural
industries. Employing intelligent automated precision livestock farming systems
that can count animals, track the animals and their poses will raise
productivity and significantly reduce the heavy burden on its already limited
labor pool. To achieve such intelligent systems, a large cattle video dataset
is essential in developing and training such models. However, many current
animal datasets are tailored to few tasks or other types of animals, which
result in poorer model performance when applied to cattle. Moreover, they do
not provide top-down views of cattle. To address such limitations, we introduce
CattleEyeView dataset, the first top-down view multi-task cattle video dataset
for a variety of inter-related tasks (i.e., counting, detection, pose
estimation, tracking, instance segmentation) that are useful to count the
number of cows and assess their growth and well-being. The dataset contains 753
distinct top-down cow instances in 30,703 frames (14 video sequences). We
perform benchmark experiments to evaluate the model's performance for each
task. The dataset and codes can be found at
this https URL

介绍了 CattleEyeView 数据集，这是第一个自顶向下视角的多任务牛视频数据集，可用于牛的计数、检测、姿势估计、跟踪和实例分割，以评估模型在每个任务上的性能。

CattleEyeView: 一种用于智能精准畜牧农业的多任务自上而下视角的牛数据集

CattleEyeView: A Multi-task Top-down View Cattle Dataset for Smarter  Precision Livestock Farming

Cattle farming is responsible for 8.8\% of greenhouse gas emissions
worldwide. In addition to the methane emitted due to their digestive process,
the growing need for grazing areas is an important driver of deforestation.
While some regulations are in place for preserving the Amazon against
deforestation, these are being flouted in various ways, hence the need to scale
and automate the monitoring of cattle ranching activities. Through a
partnership with \textit{Global Witness}, we explore the feasibility of
tracking and counting cattle at the continental scale from satellite imagery.
With a license from Maxar Technologies, we obtained satellite imagery of the
Amazon at 40cm resolution, and compiled a dataset of 903 images containing a
total of 28498 cattle. Our experiments show promising results and highlight
important directions for the next steps on both counting algorithms and the
data collection process for solving such challenges. The code is available at
https://github.com/IssamLaradji/cownter_strike.

通过与 Golbal Witness 的合作，本研究探讨了利用卫星图像在全球范围内跟踪和计数牛的可行性，在获取到亚马逊河地区的 40 厘米分辨率的卫星图像后，我们编译了包含 28498 头牛的 903 幅图像的数据集，并得到了令人满意的实验结果，这也揭示了日后解决此类挑战的计数算法和数据收集过程的重要方向。