Analysis of Tracking Stability and Performance Variations in Multi-Class Structural Damage Objects Under Viewpoint Changes in Disaster Environments

This study evaluates the tracking performance of structural damages in disaster environments by combining YOLOv8 detection with the BoT-SORT tracker. Cracks and exposed rebar, characterized by fine and irregular structures, showed high sensitivity to viewpoint changes, with camera motion compensation (CMC) improving IoU by +19.63% and +20.23%. For exposed rebar, the joint use of CMC and re-identification (Re-ID) further increased IDF1 by +37.73%, emphasizing the effectiveness of appearance-based matching. In contrast, delamination and concrete debris, with stable morphology and clear boundaries, exhibited limited benefits from CMC, improving IoU by +11.17% and +3.28%. Analysis of MOTA, IDF1, and HOTA confirms that fine-grained damages require motion- and appearance-based strategies, while stable types maintain high performance through detection consistency. These results highlight the importance of tailored tracking strategies for enhancing disaster-response robots and structural monitoring systems.