Evaluation of an AoI Mapping and Analysis Tool for the Identification of Visual Scan Pattern

The analysis of operator's work patterns in safety-critical domains is increasingly assisted by eye-tracking technologies. A reason is a growing demand for empirically justified assurance to ascertain an equal level of safety after the implementation of new techniques and higher levels of automation. In the field and simulation studies, head-mounted eye-tracking devices are a preferred choice because of the easy, reliable and fast startup. The downside is time-consuming work to map gaze points to the world/workplace coordinates which makes head-mounted devices applicable to a small number of episodes to analyze. This paper presents a solution called AoI Mapping and Analysis Tool (AMAT) that relies on visual features provided by the integrated scene video camera. AoI-templates can be defined from the video recording and are constantly matched for an AoI-analysis. AMAT was evaluated with a field study example from Air Traffic Control (ATC) in the tower at Linkoping City airport and a training simulator study example from Vessel Traffic Service (VTS) in Gothenburg. Example situations demonstrate the capabilities of AMAT to define AoIs, evaluate the validity of the chosen set of AoIs and export to an example analysis program Eloquence for the visualization of results. The AoI-sequence of 4 vessels encounter in the Gothenburg archipelago was shown as well as the comparison of four tower controller's sequences during the final approach. The final discussion highlights the capabilities and limitations of the implemented techniques that rely on the AKAZE point feature detection and linear homographic transformations. A major source of disturbance lies in the varying light conditions evoking under-exposure of the video camera and fast head movements.