TY  - CHAP
U1  - Konferenzveröffentlichung
A1  - Burgermeister, Dennis
A1  - Curio, Cristóbal
T1  - PedRecNet: Multi-task deep neural network for full 3D human pose and orientation estimation
T2  - 2022 IEEE Intelligent Vehicles Symposium (IV), 5-9 June 2022, Aachen, proceedings
N2  - 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.
KW  - deep learning
KW  - training
KW  - solid modeling
KW  - three-dimensional displays
KW  - head
KW  - neural networks
KW  - pose estimation
Y1  - 2022
SN  - 978-1-6654-8821-1
SB  - 978-1-6654-8821-1
U6  - https://doi.org/10.1109/IV51971.2022.9827202
DO  - https://doi.org/10.1109/IV51971.2022.9827202
SP  - 441
EP  - 448
S1  - 8
PB  - IEEE
CY  - Piscataway, NJ
ER  -