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.

翻译：户外广告（如路边广告牌）在营销活动中具有重要作用，但也可能分散驾驶员注意力，进而引发交通事故。本研究提出了一套从驾驶员视角拍摄的视频中评估路边广告牌重要性的流水线方法。我们收集并标注了新的BillboardLamac数据集，包含由佩戴眼动追踪设备的驾驶员沿预定路径行驶时拍摄的八段视频。该数据集包含广告牌的标注信息（共154个独立ID及15.5万个边界框）以及眼动注视数据。我们评估了多种目标追踪方法与YOLOv8检测器的组合性能，其中最优方法在BillboardLamac数据集上达到了38.5的HOTA指标。此外，我们训练了一个随机森林分类器，依据驾驶员注视时长将广告牌分为三类，测试准确率达75.8%。对训练后分类器的分析表明，广告牌的可视时长、显著性及尺寸是评估其重要性的最具影响力的特征。