{
  "annotation_id": "c6bfa30c-3636-43a7-9ca2-c900bccecd46",
  "date_created": "2026-02-17T05:53:08.811000Z",
  "date_modified": "2026-02-17T05:53:08.811000Z",
  "file_hash": "681f58bbeff77d08b819336cab7cd9d7c9537dd69e8b8095a43e87b1a61ee708",
  "private": false,
  "record": {
    "abstract": "Roadside billboards represent a central element of outdoor advertising, yet their presence may contribute to driver distraction and accident risk. This study introduces a fully automated pipeline for billboard detection and driver gaze duration estimation, aiming to evaluate billboard relevance without reliance on manual annotations or eye-tracking devices. Our pipeline operates in two stages: (1) a YOLO-based object detection model trained on Mapillary Vistas and fine-tuned on BillboardLamac images achieved 94% mAP@50 in the billboard detection task (2) a classifier based on the detected bounding box positions and DINOv2 features. The proposed pipeline enables estimation of billboard driver gaze duration from individual frames. We show that our method is able to achieve 68.1% accuracy on BillboardLamac when considering individual frames. These results are further validated using images collected from Google Street View.",
    "arxiv_id": "2601.07073",
    "authors": [
      "Carlos Pizarroso",
      "Zuzana Berger Haladov\u00e1",
      "Zuzana \u010cernekov\u00e1",
      "Viktor Kocur"
    ],
    "categories": [
      "cs.CV"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "title": "Billboard in Focus: Estimating Driver Gaze Duration from a Single Image",
    "url": "https://arxiv.org/abs/2601.07073",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "d63a2a33-e8eb-4391-9511-3a425fd34cd6",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}