Vision transformer-based multi-camera multi-object tracking framework for dairy cow monitoring

• Real-time dairy cow monitoring using multi-camera homography-based panoramic view • Vision Transformer model SAMURAI enhances segmentation with motion-aware memory • YOLO11-m detector achieved mAP@0.5 of 0.97 and F1 score of 0.95 on test images • Proposed system achieved 99.3% tracking accuracy and 99.7% IDF1 in complex barns • Significantly outperforms Deep SORT in cow tracking, with minimal ID switches Activity and behaviour correlate with dairy cow health and welfare, making continual and accurate monitoring crucial for disease identification and farm productivity. Manual observation and frequent assessments are laborious and inconsistent for activity monitoring. In this study, we developed a unique multi-camera, real-time tracking system for indoor-housed Holstein Friesian dairy cows. This technology uses cutting-edge computer vision techniques, including instance segmentation and tracking algorithms to monitor cow activity seamlessly and accurately. An integrated top-down barn panorama was created by geometrically aligning six camera feeds using homographic transformations. The detection phase used a refined YOLO11m model trained on an overhead cow dataset, obtaining high accuracy (mAP\@0.50 = 0.97, F1 = 0.95). SAMURAI, an upgraded Segment Anything Model 2.1, generated pixel-precise cow masks for instance segmentation utilizing zero-shot learning and motion-aware memory. Even with occlusion and fluctuating posture, a motion-aware Linear Kalman filter and IoU-based data association reliably identified cows over time for object tracking. The proposed system significantly outperformed Deep SORT Realtime. Multi-Object Tracking Accuracy (MOTA) was 98.7%, 99.3% and 97.2% in three benchmark video sequences respectively, with IDF1 scores achieving 99% and near-zero identity switches. This unified multi-camera system can track dairy cows in complex interior surroundings in real time, according to our data. The system reduces redundant detections across overlapping cameras, maintains continuity as cows move between viewpoints, with the aim of improving early sickness prediction through activity quantification and behavioural classification.