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Sensors 2015, 15(7), 15218-15245; doi:10.3390/s150715218

Articulated Non-Rigid Point Set Registration for Human Pose Estimation from 3D Sensors

School of Electrical and Computer Engineering, Oklahoma State University, Stillwater, OK 74078, USA
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Academic Editor: Vittorio M.N. Passaro
Received: 9 May 2015 / Revised: 10 June 2015 / Accepted: 16 June 2015 / Published: 29 June 2015
(This article belongs to the Section Physical Sensors)

Abstract

We propose a generative framework for 3D human pose estimation that is able to operate on both individual point sets and sequential depth data. We formulate human pose estimation as a point set registration problem, where we propose three new approaches to address several major technical challenges in this research. First, we integrate two registration techniques that have a complementary nature to cope with non-rigid and articulated deformations of the human body under a variety of poses. This unique combination allows us to handle point sets of complex body motion and large pose variation without any initial conditions, as required by most existing approaches. Second, we introduce an efficient pose tracking strategy to deal with sequential depth data, where the major challenge is the incomplete data due to self-occlusions and view changes. We introduce a visible point extraction method to initialize a new template for the current frame from the previous frame, which effectively reduces the ambiguity and uncertainty during registration. Third, to support robust and stable pose tracking, we develop a segment volume validation technique to detect tracking failures and to re-initialize pose registration if needed. The experimental results on both benchmark 3D laser scan and depth datasets demonstrate the effectiveness of the proposed framework when compared with state-of-the-art algorithms. View Full-Text
Keywords: point set registration; visible points extraction; segment volume validation; human pose estimation point set registration; visible points extraction; segment volume validation; human pose estimation
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Ge, S.; Fan, G. Articulated Non-Rigid Point Set Registration for Human Pose Estimation from 3D Sensors. Sensors 2015, 15, 15218-15245.

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