TY - CHAP U1 - Konferenzveröffentlichung A1 - Ludl, Dennis A1 - Gulde, Thomas A1 - Thalji, Salma A1 - Curio, Cristóbal T1 - Using simulation to improve human pose estimation for corner cases T2 - 21st International Conference on Intelligent Transportation Systems ​(ITSC) November 4-7, 2018 ​ Maui, Hawaii, USA N2 - 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. Y1 - 2018 SN - 978-1-7281-0323-5 SB - 978-1-7281-0323-5 U6 - https://doi.org/10.1109/ITSC.2018.8569489 DO - https://doi.org/10.1109/ITSC.2018.8569489 SP - 3575 EP - 3582 S1 - 8 PB - IEEE CY - Piscataway, NJ ER -