Existing image/video datasets for cattle behavior recognition are mostly small, lack well-defined labels, or are collected in unrealistic controlled environments. This limits the utility of machine learning (ML) models learned from them. Therefore, we introduce a new dataset, called Cattle Visual Behaviors (CVB), that consists of 502 video clips, each fifteen seconds long, captured in natural lighting conditions, and annotated with eleven visually perceptible behaviors of grazing cattle. We use the Computer Vision Annotation Tool (CVAT) to collect our annotations. To make the procedure more efficient, we perform an initial detection and tracking of cattle in the videos using appropriate pre-trained models. The results are corrected by domain experts along with cattle behavior labeling in CVAT. The pre-hoc detection and tracking step significantly reduces the manual annotation time and effort. Moreover, we convert CVB to the atomic visual action (AVA) format and train and evaluate the popular SlowFast action recognition model on it. The associated preliminary results confirm that we can localize the cattle and recognize their frequently occurring behaviors with confidence. By creating and sharing CVB, our aim is to develop improved models capable of recognizing all important behaviors accurately and to assist other researchers and practitioners in developing and evaluating new ML models for cattle behavior classification using video data.

该研究介绍了一个名为Cattle Visual Behaviors (CVB)的视频数据集，用于牛行为识别。通过计算机视觉标注工具（CVAT）收集了502个视频剪辑，每个视频剪辑都有15秒长，并且标注了11种能够直观感知的牛的行为。研究使用预训练模型对视频中的牛进行检测和跟踪，并使用CVAT进行修正和标记，从而减少了标注的时间和精力。最后，使用SlowFast动作识别模型对该数据集进行了训练和评估，能够准确地识别牛的频繁出现的行为。

CVB：一份关于牛视觉行为的视频数据集