  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
https://github.com/AnimalEyeQ/CattleEyeView.
