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 roadside billboards）在营销活动中扮演重要角色，但也可能分散驾驶员注意力，进而引发事故。本研究提出了一种从驾驶员视角视频中评估路边广告牌显著性的分析流程。我们收集并标注了全新的BillboardLamac数据集，包含八段驾驶员佩戴眼动追踪设备沿指定路径行驶时录制的视频。该数据集包含广告牌标注（154个独立ID与15.5万个边界框）及眼动注视数据。我们评估了结合YOLOv8检测器的多种目标跟踪方法，最佳方法在BillboardLamac数据集上达到38.5 HOTA。此外，我们训练随机森林分类器，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%。经分析，广告牌可见时长、显著性和尺寸是评估其显著性时最具影响力的特征。