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Using simulation to improve human pose estimation for corner cases

  • Recognizing actions of humans, reliably inferring their meaning and being able to potentially exchange mutual social information are core challenges for autonomous systems when they directly share the same space with humans. Today’s technical perception solutions have been developed and tested mostly on standard vision benchmark datasets where manual labeling of sensory ground truth is a tedious but necessary task. Furthermore, rarely occurring human activities are underrepresented in such data leading to algorithms not recognizing such activities. For this purpose, we introduce a modular simulation framework which offers to train and validate algorithms on various environmental conditions. For this paper we created a dataset, containing rare human activities in urban areas, on which a current state of the art algorithm for pose estimation fails and demonstrate how to train such rare poses with simulated data only.

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Metadaten
Name:Ludl, Dennis; Gulde, Thomas; Thalji, Salma; Curio, Cristóbal
DOI:https://doi.org/10.1109/ITSC.2018.8569489
ISBN:978-1-7281-0323-5
Erschienen in:21st International Conference on Intelligent Transportation Systems ​(ITSC) November 4-7, 2018 ​ Maui, Hawaii, USA
Publisher:IEEE
Place of publication:Piscataway, NJ
Document Type:Conference Proceeding
Language:English
Year of Publication:2018
Pagenumber:8
First Page:3575
Last Page:3582
Dewey Decimal Classification:004 Informatik
Access Rights:Nein
Licence (German):License Logo  Lizenzbedingungen IEEE