Unrecognized hazards increase the likelihood of workplace fatalities and injuries substantially. However, recent research has demonstrated that a large proportion of hazards remain unrecognized in dynamic construction environments. Recent studies have suggested a strong correlation between viewing patterns of workers and their hazard recognition performance. Hence, it is important to study and analyze the viewing patterns of workers to gain a better understanding of their hazard recognition performance. The objective of this exploratory research is to explore hazard recognition as a visual search process to identifying various visual search factors that affect the process of hazard recognition. Further, the study also proposes a framework to develop a vision based tool capable of recording and analyzing viewing patterns of construction workers and generate feedback for personalized training and proactive safety management.
@article{arxiv.1809.00949,
title = {Automating Analysis of Construction Workers Viewing Patterns for Personalized Safety Training and Management},
author = {Idris Jeelani and Kevin Han and Alex Albert},
journal= {arXiv preprint arXiv:1809.00949},
year = {2018}
}