A deep learning based method for 3D human pose estimation from 2D fisheye images
Journal
International Conference on Intelligent User Interfaces
Date Issued
2018
Author(s)
Abstract
We propose a deep learning based method to directly estimate the human joint positions in 3D space from 2D fisheye images captured in an egocentric manner. The core of our method is a novel network architecture based on Inception-v3 [4], featuring the asymmtric convolutional filter size, the long short-term memory module, and the anthropomorphic weights on the training loss. We demonstrate our method outperform state-of-the-art method under different tasks. Our method can be helpful to develop useful deep learning network for human-machine interaction and VR/AR applications.
SDGs
Type
conference paper