# Occlusion-Aware Worker Detection in Masonry Work: Performance Evaluation of YOLOv8 and SAMURAI
###### Abstract
Addressing these limitations requires more advanced tracking methodologies that can maintain persistent worker identification even in occlusion-heavy environments [52; 53; 54]. Occlusion is a major challenge in computer vision-based construction monitoring, particularly in masonry work [48; 55]. During brick transportation, workers carrying multiple bricks may partially or fully obscure their own bodies [56], preventing conventional object detection models from maintaining continuous tracking [48]. Similarly, as the height of the masonry wall increases, parts of the worker's body become progressively occluded by the stacked bricks, leading to tracking inconsistencies and data loss in movement analysis [57]. The inability to accurately track workers under these conditions results in gaps in productivity assessments and unreliable performance metrics. For example, in a continuous monitoring situation, if a worker is frequently occluded by construction materials, the detection model may fail to recognize their presence for extended periods [58]. As a result, the system might incorrectly register the worker as inactive, leading to inaccurate productivity measurement results [38; 59]. Furthermore, the limitations of existing models can lead to misinterpretations of worker activities, inaccurate performance evaluations, and difficulties in automating construction site monitoring [15; 60]. Addressing the occlusion problem is crucial for enhancing the accuracy of automated worker tracking and ensuring reliable productivity evaluations [58; 61]. To overcome these challenges, SAM-based unified and robust zero-shot visual tracker with motion-aware instance-level memory (SAMURAI) has emerged as a promising solution [62]. Unlike conventional object detection models that process each frame independently, SAMURAI incorporates motion-aware memory selection mechanisms, enabling objects to be tracked continuously even under occlusion scenarios [62]. By integrating temporal continuity across multiple frames, SAMURAI significantly improves tracking accuracy in highly dynamic environments where objects frequently disappear and reappear due to physical obstructions [62; 63]. SAMURAI was developed to address long-term object tracking (LTOT) challenges [62], particularly in scenarios where standard object detection models fail due to occlusion [62]. Unlike YOLO, which relies solely on frame-baseddetection, SAMURAI leverages previous frame information to maintain object identities, ensuring greater tracking stability [62]. SAMURAI is based on the Segment Anything Model (SAM) and its advanced version, SAM2, incorporating a motion-aware instance-level memory selection mechanism [62]. Unlike conventional object detection models, SAMURAI utilizes SAM2's segmentation-driven framework to enhance object tracking performance, particularly in occlusion-heavy environments [62]. This approach allows SAMURAI to refine object tracking by integrating motion-aware selection and affinity-based filtering, ensuring persistent identification of occluded objects across frames [62]. Recent studies have demonstrated its potential in long-term tracking tasks, including surveillance and autonomous systems [62], suggesting that its occlusion-resistant capabilities could also be applicable to construction site monitoring. Despite the increasing adoption of SAMURAI in other industries [62], studies investigating SAMURAI's application in construction remain limited. To facilitate the adoption of SAMURAI in the construction sector, it is essential to evaluate whether it can provide a robust and reliable alternative to YOLO for real-time construction monitoring. While previous research has extensively explored YOLO-based worker tracking [64, 65], no comprehensive study has directly compared the performance of YOLO and SAMURAI in masonry work.