Horse Eye Blink Detection and Classification for Equine Affective State Assessment
Researchers have developed and evaluated three methods for automatically detecting and classifying horse eye blinks from video footage. These methods, including a YOLOv12 detector, an optical flow approach, and a fine-tuned VideoMAE model, aim to identify subtle expressions indicative of pain or stress in horses. The study achieved a macro-F1 score of 0.898 for blink classification and 0.926 for blink detection, demonstrating the potential for automated equine welfare monitoring. AI
IMPACT Develops novel AI applications for animal welfare monitoring, potentially improving stress and pain detection in horses.