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Cattle farming is one of the important and profitable agriculturalindustries. Employing intelligent automated precision livestock farming systemsthat can count animals, track the animals and their poses will raiseproductivity and significantly reduce the heavy burden on its already limitedlabor pool. To achieve such intelligent systems, a large cattle video datasetis essential in developing and training such models. However, many currentanimal datasets are tailored to few tasks or other types of animals, whichresult in poorer model performance when applied to cattle. Moreover, they donot provide top-down views of cattle. To address such limitations, we introduceCattleEyeView dataset, the first top-down view multi-task cattle video datasetfor a variety of inter-related tasks (i.e., counting, detection, poseestimation, tracking, instance segmentation) that are useful to count thenumber of cows and assess their growth and well-being. The dataset contains 753distinct top-down cow instances in 30,703 frames (14 video sequences). Weperform benchmark experiments to evaluate the model's performance for eachtask. The dataset and codes can be found athttps://github.com/AnimalEyeQ/CattleEyeView.

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