Real-world Mapping of Gaze Fixations Using Instance Segmentation for Road Construction Safety Applications
This work addresses safety risks for construction workers by providing a tool to analyze hazard recognition performance, though it is incremental as it applies existing computer vision techniques to a specific domain.
The paper tackles the problem of unrecognized hazards in road construction by automatically mapping workers' gaze fixations to predefined areas of interest using instance segmentation and transfer learning, enabling analysis of viewing behaviors and attention distribution.
Research studies have shown that a large proportion of hazards remain unrecognized, which expose construction workers to unanticipated safety risks. Recent studies have also found that a strong correlation exists between 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 to gain 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.