TITLE: CVB: A Video Dataset of Cattle Visual Behaviors 

ABSTRACT: 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.
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.
CVB: A Video Dataset of Cattle Visual Behaviors
http://arxiv.org/abs/2305.16555v2