# Area of Interest Tracking Techniques for Driving Scenarios Focusing on Visual Distraction Detection
## 2 Methods
### Processing of Measurement
#### 2.3.1 Tag Identification  
During our previous ET studies, we encountered limitations with the 'Pupil Labs Software' when using Pupil Labs ET glasses. The software was unable to accurately detect tags on the vehicle due to sudden obstructions or distortions in the raw video images. The tags, from the 36h11 family, were initially read using MATLAB's AprilTag function. To mark the corners of the investigated AOIs, the ID tags were positioned as follows and shown in Figure 2:  
* Area of driver's view of the road (windscreen); ID tag numbers: 6, 7, 4, 6 (from left to right down). * Centre console surface (dashboard1); ID tag number: 0, 1, 2, 3, (from left to right down). One of the challenges encountered was the inconsistent lighting and varying viewing angles, which made it difficult to identify tags in the unedited video. In CDS scenarios, it is difficult to avoid external light variations that may degrade camera images. It has been observed that alterations in lighting conditions may result in flares, optical distortions, and glare. The head-mounted ET glasses move with the subject's head, avoiding distortion of the AOI areas as the ID tag positions constantly shift and change relative to each other. Another problem is when the hand, arm or control unit (e.g., steering wheel) obscures the ID tag. These phenomena are less likely to occur in a simulation environment. In our research, we devised a novel approach to effectively identify ID tags in video frames; we named our method BAIT. This process involves the initial separation of each video frame into its constituent RGB color components. Subsequently, we implemented a binarization technique on these separated components at varying levels of sensitivity. Binarization here refers to the conversion of the color pixels into a binary format, essentially transforming them into either black or white pixels. The term'sensitivity' in this context describes the threshold value for the color intensity of a pixel to be considered black. For example, at a sensitivity setting of 0%, only pixels with the maximum color intensity value \begin{table}
\begin{tabular}{l l} \hline \hline
**Feature** & **Specification** \\ \hline Eye Tracking System & Pupil Core eye-tracking system \\ \hline Infrared (IR) Eye Cameras & 2 cameras, 120 Hz @ 400 \(\times\) 400 px \\ \hline RGB World-view camera & 1 camera, 30 Hz @ 1080p/60 Hz @ 720p, 139\({}^{\circ}\)\(\times\) 83\({}^{\circ}\) \\  & wide-angle lens \\ \hline Recording Management & Pupil Capture software v3.5.1 (Pupil Labs GmbH., Berlin, Germany) \\ \hline Workstation PC Specifications & 11 th Gen Intel(R) Core(TM) i7–11800H, 32 DDR4 RAM, NVIDIA GeForce RTX 3050 Ti Laptop GPU \\ \hline Calibration Process & Semi-automated, using calibration circles \\ \hline ID-Tag Markers & tag36h11 family printed on 50 mm \(\times\) 50 mm hard plastic plates (ID tags: 0 to 7) \\ \hline Post-processing & Pupil Player software v3.5.1 (Pupil Labs GmbH., Berlin, Germany) hereinafter called ’Pupil Labs Software’ \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Eye-tracking system datasheet. of 255 are rendered black. Conversely, at a sensitivity of 100%, even a minimal color intensity value of 1 is sufficient to turn a pixel black. The analysis includes examining the original RGB frame, taking a holistic approach. For each set of analyses, one original frame and eighteen binarized frames are considered. There are three sets at six different levels of sensitivity each. Once the frames have undergone this binarization process, we can then identify the ID tags within each frame. These identifications are conducted separately for each binarized frame, and the findings are subsequently amalgamated. Figure 3 shows the original video frame. Figure 4 show the results of our binarization process on the three separate RGB components of a frame, performed at different levels of sensitivity. The ID tags successfully identified in each iteration are accentuated in black. Figure 3: The original frame. Figure 2: ID tag positioning (around windscreen and center console).