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PedRecNet: Multi-task deep neural network for full 3D human pose and orientation estimation

  • We present a multitask network that supports various deep neural network based pedestrian detection functions. Besides 2D and 3D human pose, it also supports body and head orientation estimation based on full body bounding box input. This eliminates the need for explicit face recognition. We show that the performance of 3D human pose estimation and orientation estimation is comparable to the state-of-the-art. Since very few data sets exist for 3D human pose and in particular body and head orientation estimation based on full body data, we further show the benefit of particular simulation data to train the network. The network architecture is relatively simple, yet powerful, and easily adaptable for further research and applications.

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Metadaten
Author of HS ReutlingenCurio, Cristobal; Burgermeister, Dennis
DOI:https://doi.org/10.1109/IV51971.2022.9827202
ISBN:978-1-6654-8821-1
Publisher:IEEE
Place of publication:Piscatway, NJ
Document Type:Conference Proceeding
Language:English
Year of Publication:2022
Tag:deep learning; head; neural networks; pose estimation; solid modeling; three-dimensional displays; training
Page Number:8
First Page:441
Last Page:448
DDC classes:004 Informatik
Open Access?:Nein
Licence (German):License Logo  Lizenzbedingungen IEEE