CVB: A Video Dataset of Cattle Visual Behaviors
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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. 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. The dataset is presented in the form of following three sub-directories. 1. raw_frames: contains 450 frames in each sub folder, representing 15 sec video, taking at a frames rate of 30 FPS, 2. annotations: contains the json files corresponding to the raw_frames folder. We have one json file for one video, containing the bounding box annotations for each cattle and their associated behaviors, and 3. CVB_in_AVA_format: contains the CVB data in the standard AVA dataset format which we have used to apply SlowFast model.
现有用于牛只行为识别的图像/视频数据集大多存在规模偏小、标注规范度不足，或采集于非现实受控环境等问题，这极大限制了基于此类数据集训练得到的机器学习（Machine Learning, ML）模型的实际应用价值。为此，我们构建并发布了一款全新数据集——牛只视觉行为数据集（Cattle Visual Behaviors，CVB）。该数据集包含502段时长为15秒的视频片段，均采集于自然光照环境下，并针对放牧牛只的11种可视觉识别的行为完成了标注。通过构建与共享CVB数据集，我们的目标是开发出能够精准识别所有关键牛只行为的优化模型，同时助力其他研究人员与从业者利用视频数据开发、评估用于牛只行为分类的新型机器学习模型。本数据集包含以下三个子目录： 1. raw_frames：每个子文件夹内包含450帧图像，对应一段15秒的视频，采集帧率为30 FPS（Frames Per Second）； 2. annotations：包含与raw_frames文件夹对应的JSON格式标注文件，每段视频对应一个JSON文件，内含每头肉牛的边界框标注及其关联行为信息； 3. CVB_in_AVA_format：采用标准AVA数据集格式存储的CVB数据集，供我们调用SlowFast模型开展实验使用。
