Name
Title | ||
|---|---|---|
Improving Full-Body Pose Estimation from a Small Sensor Set Using Artificial Neural Networks and a Kalman Filter |
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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1609/aaai.v33i01.330110063 | AAAI |
Field | DocType | Volume |
State prediction,Computer science,Algorithm,Kalman filter,Pose,Coherence (physics),Artificial intelligence,Jitter,Artificial neural network,Machine learning | Conference | 33 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Frank J. Wouda | 1 | 0 | 1.35 |
| Matteo Giuberti | 2 | 38 | 6.35 |
| Giovanni Bellusci | 3 | 17 | 3.50 |
| Bert-Jan F van Beijnum | 4 | 45 | 3.37 |
| Peter H. Veltink | 5 | 291 | 42.38 |