Evaluating the Significance of Outdoor Advertising from Driver's Perspective Using Computer Vision
This addresses the need to assess advertising impact and driver distraction in road safety, but is incremental as it applies existing methods to a new dataset.
The study tackled the problem of evaluating roadside billboard significance from a driver's perspective by proposing a pipeline using computer vision, resulting in a best object tracking method achieving 38.5 HOTA on a new dataset and a classifier with 75.8% test accuracy.
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.