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SynPeDS: A Synthetic Dataset for Pedestrian Detection in Ur..:

, In: Proceedings of the 6th ACM Computer Science in Cars Symposium,

Stauner, Thomas ; Blank, Frederik ; Fürst, Michael... - p. 1-10 , 2022

Link: https://dl.acm.org/doi/10.1145/3568160.3570230

RT 
T1 <cTitle>Proceedings of the 6th ACM Computer Science in Cars Symposium</cTitle>: T1 <Title>SynPeDS: A Synthetic Dataset for Pedestrian Detection in Urban Traffic Scenes</Title>

UL https://suche.suub.uni-bremen.de/peid=acm-3570230&Exemplar=1&LAN=DE
				
A1 Stauner, Thomas
A1 Blank, Frederik
A1 Fürst, Michael
A1 Günther, Johannes
A1 Hagn, Korbinian
A1 Heidenreich, Philipp
A1 Huber, Markus
A1 Knerr, Bastian
A1 Schulik, Thomas
A1 Leiß, Karl-Ferdinand
PB ACM
YR 2022
K1 Automated driving
K1 Pedestrian detection
K1 Computing methodologies
K1 Artificial intelligence
K1 Computer vision
K1 Software and its engineering
K1 Software organization and properties
K1 Extra-functional properties
K1 Software safety
K1 Computer graphics
K1 Rendering
K1 Computer vision tasks
K1 Vision for robotics
K1 Machine learning
K1 Learning paradigms
K1 Supervised learning
SP 1
OP 10
LK http://dx.doi.org/https://dl.acm.org/doi/10.1145/3568160.3570230
DO https://dl.acm.org/doi/10.1145/3568160.3570230
SF ELIB - SuUB Bremen

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