Authors:
João Borges
1
;
Bruno Oliveira
1
;
Helena Torres
1
;
Nelson Rodrigues
1
;
Sandro Queirós
1
;
2
;
3
;
Maximilian Shiller
4
;
Victor Coelho
5
;
Johannes Pallauf
4
;
José Henrique Mendes
1
and
Jaime C. Fonseca
1
Affiliations:
1
Algoritmi Center, University of Minho, Guimarães, Portugal
;
2
ICVS/3B’s – PT Government Associate Laboratory, Braga/Guimarães, Portugal
;
3
Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal
;
4
Bosch Engineering GmbH, Abstatt, Germany
;
5
Bosch Car Multimédia S.A., Braga, Portugal 62Ai - Polytechnical Institute of Cávado and Ave, Barcelos, Portugal
Keyword(s):
Automotive Applications, Synthetic Dataset Generation, Supervised Learning, Human Pose Estimation.
Abstract:
In this paper, a toolchain for the generation of realistic synthetic images for human body pose detection in an in-car environment is proposed. The toolchain creates a customized synthetic environment, comprising human models, car, and camera. Poses are automatically generated for each human, taking into account a per-joint axis Gaussian distribution, constrained by anthropometric and range of motion measurements. Scene validation is done through collision detection. Rendering is focused on vision data, supporting time-of-flight (ToF) and RGB cameras, generating synthetic images from these sensors. Ground-truth data is then generated, comprising the car occupants’ body pose (2D/3D), as well as full body RGB segmentation frames with different body parts’ labels. We demonstrate the feasibility of using synthetic data, combined with real data, to train distinct machine learning agorithms, demonstrating the improvement in their algorithmic accuracy for the in-car scenario.