CVB: A Video Dataset of Cattle Visual Behaviors

Brief description

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.
Lineage: 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.