Human pose estimation based on manifold Gaussian process with depth images
Yuhui Xie, Ruirui Ji, Wen Wang, Zhilian Wang · Thirteenth International Conference on Graphics and Image Processing (ICGIP 2021) · 2022
At present, human pose estimation with depth images faces some challenges. Methods based on deep learning perform well but rely on massive amounts of data, while traditional machine learning methods are simple to implement but depend on feature extraction and have low accuracy. To deal with them, this paper proposes a novel method based on the Manifold Gaussian Process, which combines tomographic image denoising and feature fusion to solve human pose estimation with depth images. The experimental prediction accuracy on ITOP datasets outperforms other machine learning methods, achieving 83.3% and 77.9% for full body from the front view and top view respectively, which proves the effectiveness of Manifold Gaussian Process on human pose estimation with depth images.