YOLOv8-SAMURAI: A Hybrid Tracking Framework for Ladder Worker Safety Monitoring in Occlusion Scenarios

Monitoring worker safety during ladder operations at construction sites is challenging due to occlusion, where workers are partially or fully obscured by objects or other workers, and overlapping, which makes individual tracking difficult. Traditional object detection models, such as YOLOv8, struggle to maintain tracking continuity under these conditions. To address this, we propose an integrated framework combining YOLOv8 for initial object detection and the SAMURAI tracking algorithm for enhanced occlusion handling. The system was evaluated across four occlusion scenarios: non-occlusion, minor occlusion, major occlusion, and multiple worker overlap. The results indicate that, while YOLOv8 performs well in non-occluded conditions, the tracking accuracy declines significantly under severe occlusions. The integration of SAMURAI improves tracking stability, object identity preservation, and robustness against occlusion. In particular, SAMURAI achieved a tracking success rate of 94.8% under major occlusion and 91.2% in multiple worker overlap scenarios—substantially outperforming YOLOv8 alone in maintaining tracking continuity. This study demonstrates that the YOLOv8-SAMURAI framework provides a reliable solution for real-time safety monitoring in complex construction environments, offering a foundation for improved compliance monitoring and risk mitigation.