Using Simulation to Improve Human Pose Estimation for Corner Cases
Using Simulation to Improve Human Pose Estimation for Corner Cases
- New search for: Ludl, Dennis
- New search for: Gulde, Thomas
- New search for: Thalji, Salma
- New search for: Curio, Cristobal
- New search for: Ludl, Dennis
- New search for: Gulde, Thomas
- New search for: Thalji, Salma
- New search for: Curio, Cristobal
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.
Document information
Using Simulation to Improve Human Pose Estimation for Corner Cases
2018-11-01
1259697 byte
Conference paper
Electronic Resource
English
Similar titles
Hand Pose Estimation for Hand-Object Interaction Cases using Augmented Autoencoder
| German Aerospace Center (DLR) | 2020
| IEEE | 2018
Human Pose Estimation Using Partial Configurations and Probabilistic Regions
| British Library Online Contents | 2007
Pose Estimation of Human Face using Synthesized Model Images
| British Library Conference Proceedings | 1994