Multi-Object Tracking of Korean Cattle (Hanwoo) in High-Density Barns Using Multi-Temporal Window Repair (MTW-Repair)

Reliable individual tracking supports animal behavior monitoring in precision livestock farming. However, tracking cattle using a single camera in high-density barns is challenging because of frequent occlusion, scale variation, and irregular movement. This study presents a lightweight multi-object tracking framework for densely housed Hanwoo cattle, combining a Hanwoo-trained YOLOv8n detector with appearance-free BoT-SORT association. Frame sampling reduces the detection workload, while an offline Multi-Temporal Window Repair (MTW-Repair) procedure reconnects fragmented trajectories. The framework was evaluated using short annotated video clips and a continuous 215 min barn recording. In the short clips, appearance-free BoT-SORT outperformed ByteTrack while maintaining comparable throughput, and MTW-Repair reduced identity switches across all evaluated frame-sampling intervals. In the long recording, MTW-Repair reduced the total number of generated identities for both trackers. Manual verification of 50 randomly sampled ByteTrack merge events yielded a merge precision of 84.0%. Appearance-based re-identification offered only marginal accuracy gains at a higher computational cost. These findings support the potential of frame sampling and offline trajectory repair for computationally efficient cattle monitoring.