Horse Eye Blink Detection and Classification for Equine Affective State Assessment
Quick Answer
This study introduces three automated methods for detecting horse eye blinks, achieving a macro-F1 score of 0.898 for classification and 0.926 for binary detection.
Quick Take
This study introduces three automated methods for detecting horse eye blinks, achieving a macro-F1 score of 0.898 for classification and 0.926 for binary detection. The methods include a YOLOv12 detector, optical flow thresholding, and a fine-tuned VideoMAE model, highlighting the challenges and potential of automated equine welfare monitoring.
Key Points
- Developed three methods for automated horse blink detection from video.
- Achieved macro-F1 score of 0.898 in blink classification.
- Binary blink detection reached a score of 0.926.
- Methods include YOLOv12, optical flow thresholding, and VideoMAE.
- Highlights the challenges of detecting subtle equine facial action units.
Article Excerpt
From source RSS / original summaryarXiv:2606. 05458v1 Announce Type: new Abstract: Automated detection of equine facial action units (AUs) is a promising yet under-explored avenue for pain and affective state assessment in horses. Half and full-blink movements are recognised indicators of pain and stress, but as micro-expressions, their subtle, fine-grained nature makes them easily missed by the naked eye and only discernible through frame-by-frame video inspection, making reliable automated detection from video a particularly demanding task.
We develop and evaluate three methods for automated blink classification from horse videos: a frame-based YOLOv12 detector, an optical flow magnitude thresholding approach, and a fine-tuned VideoMAE model, tested on a publicly available dataset. We achieve a macro-F1 score of 0. 898 when doing blink classification and 0. 926 on binary blink detection. Our results highlight both the potential and the inherent challenges of fine-grained AU detection for equine welfare monitoring.
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