3D driver pose estimation based on joint 2D–3D network

Abstract

Three‐dimensional (3D) driver pose estimation is a promising and challenging problem for computer–human interaction. Recently convolutional neural networks have been introduced into 3D pose estimation, but these methods have the problem of slow running speed and are not suitable for driving scenario. In this study, the proposed method is based on two types of inputs, infrared image and point cloud obtained from time‐of‐flight camera. The authors propose a joint 2D–3D network incorporating image‐based and point‐based feature to promote the performance of 3D human pose estimation and run on a high speed. For point cloud with invalid points, the authors first do preprocess and then design a denoising module to handle this problem. Experiments on private driver data set and public Invariant‐Top View data set show that the proposed method achieves efficient and competitive performance on 3D human pose estimation.