Autor(es): Borges, João ; Oliveira, Bruno ; Torres, Helena Daniela Ribeiro ; Rodrigues, Nelson ; Queirós, Sandro Filipe Monteiro ; Shiller, Maximilian ; Coelho, Victor ; Pallauf, Johannes ; Brito, José Henrique ; Mendes, José A. ; Fonseca, Jaime C.
Data: 2020
Identificador Persistente: https://hdl.handle.net/1822/71174
Origem: RepositóriUM - Universidade do Minho
Assunto(s): Automotive Applications; Synthetic Dataset Generation; Supervised Learning; Human Pose Estimation
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.
