Human Pose Estimation from Depth Image Using Visibility Estimation and Key Points

Autor:	Gyeonghwan Kim, Sungjin Huh
Rok vydání:	2013
Předmět:	Geodesic business.industry Computer science Visibility (geometry) Computer vision Artificial intelligence Extreme point Solver business 3D pose estimation Particle filter Pose Image (mathematics)
Zdroj:	Digital Human Modeling and Applications in Health, Safety, Ergonomics, and Risk Management. Human Body Modeling and Ergonomics ISBN: 9783642391811 HCI (23)
DOI:	10.1007/978-3-642-39182-8_39
Popis:	In this paper, we propose the upper body pose estimation algorithm using 3-dimensional model and depth image. The conventional ICP algorithm is modified by adding visibility estimation and key points - extreme points and elbow locations. The visibility estimation keeps occluded points from participating in pose estimation to alleviate the affection of self-occlusion problem. Introduction of extreme points and elbow locations, which are extracted using geodesic distance map and particle filter, improves the accuracy of pose estimation result. The optimal parameters of the model are obtained from nonlinear mathematical optimization solver. The experimental results show that the proposed method accurately estimates the various human poses with self-occlusion.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::4e7ab07ee327488ce807b4672b8f8271 https://doi.org/10.1007/978-3-642-39182-8_39 Zobrazit plný text záznamu