Decoding DOOH viewability using YOLO for privacy-friendly human silhouette identification on LiDAR point clouds
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Autor:innen
Autor:in (Körperschaft)
Publikationsdatum
2024
Typ der Arbeit
Studiengang
Sammlung
Typ
04B - Beitrag Konferenzschrift
Herausgeber:innen
Herausgeber:in (Körperschaft)
Betreuer:in
Übergeordnetes Werk
2024 12th International Symposium on Digital Forensics and Security (ISDFS)
Themenheft
DOI der Originalpublikation
Link
Reihe / Serie
Reihennummer
Jahrgang / Band
Ausgabe / Nummer
Seiten / Dauer
1-6
Patentnummer
Verlag / Herausgebende Institution
IEEE
Verlagsort / Veranstaltungsort
San Antonio, Texas
Auflage
Version
Programmiersprache
Abtretungsempfänger:in
Praxispartner:in/Auftraggeber:in
Zusammenfassung
Traditional methods for measuring digital out-of-home (DOOH) advertising effectiveness often rely on static data or camera footage, leading to limitations in accuracy and real-time insights. This research proposes a novel approach that leverages the combined power of LiDAR technology and YOLOv8, a state-of-the-art object detection model, to achieve precise and privacy-friendly human silhouette identification for DOOH performance measurement. By extracting 3D point cloud data from LiDAR sensors and employing YOLOv8's efficient object detection capabilities, the model accurately identifies and tracks pedestrians in the vicinity of DOOH displays. This information, combined with LiDAR's performance under varying weather and lighting conditions, offers a significant improvement over traditional methods, providing advertisers with valuable real-time data on audience engagement and campaign effectiveness. The comparison with the same model performance trained on a standard MC-COCO 2017 dataset presented comparable accuracy but faster inference times. Furthermore, the focus on LiDAR data ensures privacy by avoiding the use of facial recognition or other sensitive personal information. This research demonstrates the feasibility and potential of LiDAR-based human silhouette identification for DOOH performance measurement, paving the way for a more data-driven and effective advertising landscape.
Schlagwörter
Fachgebiet (DDC)
Veranstaltung
12th International Symposium on Digital Forensics and Security (ISDFS)
Startdatum der Ausstellung
Enddatum der Ausstellung
Startdatum der Konferenz
29.04.2024
Enddatum der Konferenz
30.04.2024
Datum der letzten Prüfung
ISBN
979-8-3503-3036-6
979-8-3503-3037-3
979-8-3503-3037-3
ISSN
Sprache
Englisch
Während FHNW Zugehörigkeit erstellt
Ja
Zukunftsfelder FHNW
Publikationsstatus
Veröffentlicht
Begutachtung
Peer-Review der ganzen Publikation
Open Access-Status
Closed
Lizenz
Zitation
Forster, A., Lucheroni, C., & Gürtler, S. (2024). Decoding DOOH viewability using YOLO for privacy-friendly human silhouette identification on LiDAR point clouds. 2024 12th International Symposium on Digital Forensics and Security (ISDFS), 1–6. https://doi.org/10.1109/ISDFS60797.2024.10527261