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 language | English |
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Title of host publication | AAAI-19/IAAI-19/EAAI-19 Proceedings |
Place of Publication | Palo Alto, CA |
Publisher | AAAI |
Pages | 10063-10064 |
Number of pages | 2 |
ISBN (Print) | 978-1-57735-809-1 |
Publication status | Published - 17 Jul 2019 |
Event | 33rd AAAI Conference on Artificial Intelligence, AAAI 2019 - Hilton Hawaiian Village, Honolulu, United States Duration: 27 Jan 2019 → 1 Feb 2019 Conference number: 33 https://aaai.org/Conferences/AAAI-19/ |
Publication series
Name | Proceedings of the AAAI Conference on Artificial Intelligence |
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Publisher | AAAI Press |
Volume | 33 |
ISSN (Print) | 2159-5399 |
ISSN (Electronic) | 2374-3468 |
Conference
Conference | 33rd AAAI Conference on Artificial Intelligence, AAAI 2019 |
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Abbreviated title | AAAI-19 |
Country/Territory | United States |
City | Honolulu |
Period | 27/01/19 → 1/02/19 |
Internet address |