Read My Ears! Horse Ear Movement Detection for Equine Affective State Assessment

TL;DR

This paper develops and evaluates deep learning and optical flow methods for detecting ear movements in horses, aiming to automate affective state assessment and improve welfare diagnostics.

Contribution

It introduces automated ear movement detection techniques using deep learning and optical flow, achieving high accuracy and facilitating practical applications in horse welfare.

Findings

87.5% classification accuracy on a public dataset

Deep learning combined with recurrent neural networks effective for video classification

Potential for improved equine affective state detection

Abstract

The Equine Facial Action Coding System (EquiFACS) enables the systematic annotation of facial movements through distinct Action Units (AUs). It serves as a crucial tool for assessing affective states in horses by identifying subtle facial expressions associated with discomfort. However, the field of horse affective state assessment is constrained by the scarcity of annotated data, as manually labelling facial AUs is both time-consuming and costly. To address this challenge, automated annotation systems are essential for leveraging existing datasets and improving affective states detection tools. In this work, we study different methods for specific ear AU detection and localization from horse videos. We leverage past works on deep learning-based video feature extraction combined with recurrent neural networks for the video classification task, as well as a classic optical flow based approach. We achieve 87.5% classification accuracy of ear movement presence on a public horse video dataset, demonstrating the potential of our approach. We discuss future directions to develop these systems, with the aim of bridging the gap between automated AU detection and practical applications in equine welfare and veterinary diagnostics. Our code will be made publicly available at https://github.com/jmalves5/read-my-ears.