# Dynamics are Important for the Recognition of Equine Pain in Video

arXiv: https://arxiv.org/abs/1901.02106 · HTML: https://mlsanity.com/paper/1901.02106/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jan 2019.

## Built on by (most cited first, 8 of 8)

- [Adversarial Transfer Attacks With Unknown Data and Class Overlap](https://mlsanity.com/paper/2109.11125/) (2021)
- [Automated Detection of Equine Facial Action Units](https://mlsanity.com/paper/2102.08983/) (2021)
- [Sharing Pain: Using Pain Domain Transfer for Video Recognition of Low Grade Orthopedic Pain in Horses](https://mlsanity.com/paper/2105.10313/) (2021)
- [Equine Pain Behavior Classification via Self-Supervised Disentangled Pose Representation](https://mlsanity.com/paper/2108.13258/) (2021)
- [Going Deeper than Tracking: a Survey of Computer-Vision Based Recognition of Animal Pain and Affective States](https://mlsanity.com/paper/2206.08405/) (2022)
- [Deep Learning Models for Automated Classification of Dog Emotional States from Facial Expressions](https://mlsanity.com/paper/2206.05619/) (2022)
- [Read My Ears! Horse Ear Movement Detection for Equine Affective State Assessment](https://mlsanity.com/paper/2505.03554/) (2025)
- [Video-based estimation of pain indicators in dogs](https://mlsanity.com/paper/2209.13296/) (2022)

## Abstract (arXiv)

A prerequisite to successfully alleviate pain in animals is to recognize it, which is a great challenge in non-verbal species. Furthermore, prey animals such as horses tend to hide their pain. In this study, we propose a deep recurrent two-stream architecture for the task of distinguishing pain from non-pain in videos of horses. Different models are evaluated on a unique dataset showing horses under controlled trials with moderate pain induction, which has been presented in earlier work. Sequential models are experimentally compared to single-frame models, showing the importance of the temporal dimension of the data, and are benchmarked against a veterinary expert classification of the data. We additionally perform baseline comparisons with generalized versions of state-of-the-art human pain recognition methods. While equine pain detection in machine learning is a novel field, our results surpass veterinary expert performance and outperform pain detection results reported for other larger non-human species.