# Video-based cattle identification and action recognition

arXiv: https://arxiv.org/abs/2110.07103 · HTML: https://mlsanity.com/paper/2110.07103/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 23 references that are in the map.
- First posted on arXiv 14 Oct 2021.

## Builds on (most cited first, 8 of 8)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Cascade R-CNN: Delving into High Quality Object Detection](https://mlsanity.com/paper/1712.00726/) (2017)
- [Temporal Segment Networks: Towards Good Practices for Deep Action Recognition](https://mlsanity.com/paper/1608.00859/) (2016)
- [A Short Note about Kinetics-600](https://mlsanity.com/paper/1808.01340/) (2018)
- [Actions as Moving Points](https://mlsanity.com/paper/2001.04608/) (2020)
- [Towards Improving Spatiotemporal Action Recognition in Videos](https://mlsanity.com/paper/2012.08097/) (2020)

## Built on by (most cited first, 7 of 7)

- [Application of deep learning for livestock behaviour recognition: A systematic literature review](https://mlsanity.com/paper/2310.13483/) (2023)
- [CVB: A Video Dataset of Cattle Visual Behaviors](https://mlsanity.com/paper/2305.16555/) (2023)
- [Holstein-Friesian Re-Identification using Multiple Cameras and Self-Supervision on a Working Farm](https://mlsanity.com/paper/2410.12695/) (2024)
- [Real-Time Cattle Interaction Recognition via Triple-stream Network](https://mlsanity.com/paper/2209.02241/) (2022)
- [Livestock feeding behaviour: A review on automated systems for ruminant monitoring](https://mlsanity.com/paper/2312.09259/) (2023)
- [A Review on Coarse to Fine-Grained Animal Action Recognition](https://mlsanity.com/paper/2506.01214/) (2025)
- [Deep Learning Approach for RealTime Contraction Detection in Laboring Dairy Cattle](https://mlsanity.com/paper/2610.13242/) (2026)

## Abstract (arXiv)

We demonstrate a working prototype for the monitoring of cow welfare by automatically analysing the animal behaviours. Deep learning models have been developed and tested with videos acquired in a farm, and a precision of 81.2% has been achieved for cow identification. An accuracy of 84.4% has been achieved for the detection of drinking events, and 94.4% for the detection of grazing events. Experimental results show that the proposed deep learning method can be used to identify the behaviours of individual animals to enable automated farm provenance. Our raw and ground-truth dataset will be released as the first public video dataset for cow identification and action recognition. Recommendations for further development are also provided.