Image hashing-based shallow object tracker for construction worker monitoring in scaffolding scenes

Abstract

Multiple Object Tracking (MOT) has potential applications in construction site safety man- agement, particularly for the individualized assessment of scaffold workers' safety status. However, its accuracy can be degraded due to inconsistent detection results and insuffi- cient object association capabilities. This paper addresses these challenges by proposing an object tracking method, named as the Shallow Cascaded Buffered Intersection over Union (Shallow C-BIoU). This non-machine learning-based tracker employs a color hash- ing technique to enhance tracking performance. Employing rigorous evaluation metrics, the experimental results underscore the efficacy of the proposed method. Compared to state-of- the-art algorithms, the Shallow C-BIoU method improved association performance by 7.56%, reducing 52.44% of falsely assigned tracking IDs. Consequently, this paper contributes to the development of reliable object trackers, thereby advancing monitoring technologies for construction management purposes.