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 -