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Titel:

RoadSense3D: A Framework for Roadside Monocular 3D Object Detection

Dokumenttyp:
Konferenzbeitrag
Art des Konferenzbeitrags:
Textbeitrag / Aufsatz
Autor(en):
Carta, Salvatore; Castrillón-Santana, Modesto; Marras, Mirko; Mohamed, Sondos; Podda, Alessandro Sebastian; Saia, Roberto; Sau, Marco; Zimmer, Walter
Seitenangaben Beitrag:
8
Abstract:
Utilizing monocular cameras for 3D object understanding is widely recognized as a cost-effective approach, spanning applications such as autonomous driving, augmented/virtual reality or roadside monitoring. Despite recent progress, persistent challenges arise in creating generalized models adaptable to unforeseen scenarios and diverse camera configurations. In this work, we focus on the task of monocular 3D object detection within roadside environments. To begin, we introduce a versatile methodo...     »
Stichworte:
Monocular Perception, 3D Object Detection, Autonomous Driving, Roadside Sensors, Intelligent Transportation Systems, Dataset, Traffic Monitoring
Dewey-Dezimalklassifikation:
000 Informatik, Wissen, Systeme
Herausgeber:
ACM
Kongress- / Buchtitel:
Adjunct Proceedings of the 32nd International ACM Conference on User Modeling, Adaptation and Personalization
Datum der Konferenz:
1 - 4 July 2024
Verlag / Institution:
ACM
Publikationsdatum:
27.06.2024
Jahr:
2024
Quartal:
2. Quartal
Jahr / Monat:
2024-06
Monat:
Jun
Seiten:
8
Reviewed:
ja
Sprache:
en
Volltext / DOI:
doi:10.1145/3631700.3665236
WWW:
https://dl.acm.org/doi/10.1145/3631700.3665236
TUM Einrichtung:
School of Computation, Information and Technology
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