Recent studies have also found that a strong correlation existsbetween viewing patterns of workers, captured using eye-tracking devices, and their hazard recognition performance. Therefore, it is important to analyze the viewing patterns of workers togain a better understanding of their hazard recognition performance. This paper proposes a method that can automatically map the gaze fixations collected using a wearable eye-tracker to the predefined areas of interests. The proposed method detects these areas or objects (i.e., hazards) of interests through a computer vision-based segmentation technique and transfer learning. The mapped fixation data is then used to analyze the viewing behaviors of workers and compute their attention distribution. The proposed method is implemented on an under construction road as a case study to evaluate the performance of the proposed method. Keywords: Hazard recognition, road construction safety, transfer learning, eye-tracking, machine vision 1 INTRODUCTION With an average of nine fatalities every day, construction is one of the most dangerous industries for which to work (1).