# Area of Interest Tracking Techniques for Driving Scenarios Focusing on Visual Distraction Detection
## 1 Introduction
### Area of Interest  
Several key studies stand out in the literature on AOI detection in various applications. Early research has demonstrated the importance of ET in evaluating interface usability using eye movements, establishing a methodology that has become fundamental in usability studies [21]. This work emphasized the importance of understanding how users interact with interfaces, guiding improvements in design to enhance user experience. In one study researchers highlight differences in gaze patterns between natural environments and lab settings, emphasizing the need to consider natural settings in ET research as it significantly influences gaze behavior [22]. This study underscored the variability of eye movement data and the importance of designing experiments that mimic real-world settings as closely as possible. Another study tackled the area-of-interest problem in ET research by proposing a noise-robust solution for analyzing gaze data, especially when dealing with faces and sparse stimuli. Their methodology significantly contributed to improving the accuracy and reliability of ET analyses across multiple disciplines [23]. Another study introduces a specialized system for ET in video lecture contexts, showcasing its utility in enhancing educational research through engagement analysis and cognitive-process understanding [24]. The research on dynamic AOIs in ET incorporates various methodologies. New methods were introduced for filtering eye movements from dynamic areas of interest, marking a significant advance in real-time ET analysis [25]. The methodology allows for a more precise and automated analysis of gaze data in scenarios where the objects of interest are not static [20]. One study presented guidelines for integrating dynamic AOIs in setups involving moving objects, such as aircraft [26]. This study is crucial for research areas requiring automated and structured analysis of eye movements in dynamic environments, offering a blueprint for setting up such experiments. Another method explores using ArUco fiducial markers to map gaze data in dynamic settings, resolving issues of object occlusion and overlap, and also improves the accuracy of gaze tracking in complex environments [27]. A different approach introduces an open-source software for determining dynamic AOIs, enhancing tracking in studies with moving stimuli [28]. From a signal detection perspective, one study investigates the impact of area of interest (AOI) size on measuring object attention and cognitive processes. The findings contribute to a better understanding of the factors that influence the interpretation of ET data [29]. Some presented a toolkit for wide-screen dynamic AOI measurements using the Pupil Labs Core Eye Tracker, applicable in psychology and transportation research, such as multi-display driving simulators [30]. This toolkit expands the capabilities of researchers to conduct sophisticated analyses of eye movements in diverse and dynamic visual environments. These studies collectively illustrate the diverse applications and advancements in AOI detection using ET technology.