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.

通过评估司机视角下的路边广告牌的重要性，本研究提出了一种评估路边广告牌重要性的流程。研究人员收集并注释了一个新的 BillboardLamac 数据集，其中包括由佩戴眼动仪的司机沿预定路径驾驶期间拍摄的 8 个视频。该数据集包括了 154 个唯一标识和 155,000 个边界框的广告牌注释，以及眼球注视数据。通过将不同的目标跟踪方法与 YOLOv8 检测器结合使用，我们评估了识别路边广告牌的最佳方法，其在 BillboardLamac 上实现了 38.5 的 HOTA。此外，我们训练了一个随机森林分类器，根据司机注视的持续时间将广告牌分为三类，达到了 75.8％的测试准确率。对训练好的分类器的分析表明，广告牌的可见持续时间、显著性和尺寸是评估广告牌重要性时最有影响的特征。