  Outdoor advertising, such as roadside billboards, plays a significant role in
marketing campaigns but can also be a distraction for drivers, potentially
leading to accidents. In this study, we propose a pipeline for evaluating the
significance of roadside billboards in videos captured from a driver's
perspective. We have collected and annotated a new BillboardLamac dataset,
comprising eight videos captured by drivers driving through a predefined path
wearing eye-tracking devices. The dataset includes annotations of billboards,
including 154 unique IDs and 155 thousand bounding boxes, as well as eye
fixation data. We evaluate various object tracking methods in combination with
a YOLOv8 detector to identify billboard advertisements with the best approach
achieving 38.5 HOTA on BillboardLamac. Additionally, we train a random forest
classifier to classify billboards into three classes based on the length of
driver fixations achieving 75.8% test accuracy. An analysis of the trained
classifier reveals that the duration of billboard visibility, its saliency, and
size are the most influential features when assessing billboard significance.
