Improving Full-Body Pose Estimation from a Small Sensor Set Using Artificial Neural Networks and a Kalman Filter

Frank J. Wouda, Matteo Giuberti, Giovanni Bellusci, Bert-Jan F. van Beijnum, Peter H. Veltink

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

23 Downloads (Pure)

Abstract

Previous research has shown that estimating full-body poses from a minimal sensor set using a trained ANN without explicitly enforcing time coherence has resulted in output pose sequences that occasionally show undesired jitter. To mitigate such effect, we propose to improve the ANN output by combining it with a state prediction using a Kalman Filter. Preliminary results are promising, as the jitter effects are diminished. However, the overall error does not decrease substantially.
Original languageEnglish
Title of host publicationAAAI-19/IAAI-19/EAAI-19 Proceedings
Place of PublicationPalo Alto, CA
PublisherAAAI Press
Pages10063-10064
Number of pages2
ISBN (Print)978-1-57735-809-1
Publication statusPublished - 17 Jul 2019
Event33rd AAAI Conference on Artificial Intelligence, AAAI 2019 - Hilton Hawaiian Village, Honolulu, United States
Duration: 27 Jan 20191 Feb 2019
Conference number: 33
https://aaai.org/Conferences/AAAI-19/

Publication series

NameProceedings of the AAAI Conference on Artificial Intelligence
PublisherAAAI Press
Volume33
ISSN (Print)2159-5399
ISSN (Electronic)2374-3468

Conference

Conference33rd AAAI Conference on Artificial Intelligence, AAAI 2019
Abbreviated titleAAAI-19
CountryUnited States
CityHonolulu
Period27/01/191/02/19
Internet address

Fingerprint Dive into the research topics of 'Improving Full-Body Pose Estimation from a Small Sensor Set Using Artificial Neural Networks and a Kalman Filter'. Together they form a unique fingerprint.

Cite this