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Unsupervised articulated skeleton extraction from point set sequences captured by a single depth camera

conference contribution
posted on 2018-01-01, 00:00 authored by Xuequan Lu, H Chen, S K Yeung, Z Deng, W Chen
How to robustly and accurately extract articulated skeletons from point set sequences captured by a single consumer-grade depth camera still remains to be an unresolved challenge to date. To address this issue, we propose a novel, unsupervised approach consisting of three contributions (steps): (i) a non-rigid point set registration algorithm to first build one-to-one point correspondences among the frames of a sequence; (ii) a skeletal structure extraction algorithm to generate a skeleton with reasonable numbers of joints and bones; (iii) a skeleton joints estimation algorithm to achieve accurate joints. At the end, our method can produce a quality articulated skeleton from a single 3D point sequence corrupted with noise and outliers. The experimental results show that our approach soundly outperforms state of the art techniques, in terms of both visual quality and accuracy.

History

Event

Association for the Advancement of Artificial Intelligence. Conference (32nd : 2018 : New Orleans, La.)

Series

Association for the Advancement of Artificial Intelligence Conference

Pagination

7226 - 7234

Publisher

AAAI Press

Location

New Orleans, La.

Place of publication

Palo Alto, Calif.

Start date

2018-02-02

End date

2018-02-07

ISBN-13

9781577358008

Language

eng

Publication classification

E1.1 Full written paper - refereed

Editor/Contributor(s)

[Unknown]

Title of proceedings

AAAI-18 : Proceedings of the 32nd AAAI Conference on Artificial Intelligence