# CVB: A Video Dataset of Cattle Visual Behaviors

arXiv: https://arxiv.org/abs/2305.16555 · HTML: https://mlsanity.com/paper/2305.16555/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 45 references that are in the map.
- First posted on arXiv 26 May 2023.

## Builds on (most cited first, 16 of 16)

- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [SlowFast Networks for Video Recognition](https://mlsanity.com/paper/1812.03982/) (2018)
- [YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors](https://mlsanity.com/paper/2207.02696/) (2022)
- [Zero-Shot Learning -- A Comprehensive Evaluation of the Good, the Bad and the Ugly](https://mlsanity.com/paper/1707.00600/) (2017)
- [AVA: A Video Dataset of Spatio-temporally Localized Atomic Visual Actions](https://mlsanity.com/paper/1705.08421/) (2017)
- [BoT-SORT: Robust Associations Multi-Pedestrian Tracking](https://mlsanity.com/paper/2206.14651/) (2022)
- [Cross-Domain Adaptation for Animal Pose Estimation](https://mlsanity.com/paper/1908.05806/) (2019)
- [Animal Kingdom: A Large and Diverse Dataset for Animal Behavior Understanding](https://mlsanity.com/paper/2204.08129/) (2022)
- [The Devil is in the Tails: Fine-grained Classification in the Wild](https://mlsanity.com/paper/1709.01450/) (2017)
- [AcinoSet: A 3D Pose Estimation Dataset and Baseline Models for Cheetahs in the Wild](https://mlsanity.com/paper/2103.13282/) (2021)
- [A Novel Dataset for Keypoint Detection of quadruped Animals from Images](https://mlsanity.com/paper/2108.13958/) (2021)
- [Florida Wildlife Camera Trap Dataset](https://mlsanity.com/paper/2106.12628/) (2021)
- [Video-based cattle identification and action recognition](https://mlsanity.com/paper/2110.07103/) (2021)
- [Voxelized 3D Feature Aggregation for Multiview Detection](https://mlsanity.com/paper/2112.03471/) (2021)
- [Adjusting for Bias with Procedural Data](https://mlsanity.com/paper/2204.01108/) (2022)
- [Livestock Monitoring with Transformer](https://mlsanity.com/paper/2111.00801/) (2021)

## Built on by (most cited first, 4 of 4)

- [Cattle-CLIP: A Multimodal Framework for Cattle Behaviour Recognition from Video](https://mlsanity.com/paper/2510.09203/) (2025)
- [8-Calves Image dataset](https://mlsanity.com/paper/2503.13777/) (2025)
- [Mouse Lockbox Dataset: Behavior Recognition for Mice Solving Lockboxes](https://mlsanity.com/paper/2505.15408/) (2025)
- [A Review on Coarse to Fine-Grained Animal Action Recognition](https://mlsanity.com/paper/2506.01214/) (2025)

## Abstract (arXiv)

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.