Name
Abstract | ||
|---|---|---|
We propose a sensor fusion technique for motion capture system. In our system, two kinds of sensors are used for mutual assistance. Four magnetic sensors (markers) are attached on the upper arms and the back of the hands for assisting twelve optical sensors, which are attached on the arms of a performer. The optical sensor information is not always complete because the optical markers can be hidden due to obstacles. In this case, magnetic sensor information is used to link discontinuous optical sensor information. We use a system identification technique for modeling the relation between the sensors' signals. Dynamic systems are constructed from input-output data. We determine the best model from the set of candidate models using the canonical system identification technique. Our approach is using a simple signal processing technique currently. In the future work, we will propose a new method using other signal processing techniques such as Wiener or Kalman filter. |
Year | DOI | Venue |
|---|---|---|
2000 | 10.1109/CA.2000.889042 | CA |
Keywords | Field | DocType |
signal processing,dynamic system,hardware,motion estimation,input output,sensor fusion,optical sensor,kalman filter,arm,animation,motion capture,system identification,virtual reality | Motion capture,Signal processing,Computer vision,Soft sensor,Kalman filter,Sensor fusion,Artificial intelligence,Electro-optical sensor,Motion estimation,Engineering,System identification | Conference |
ISSN | Citations | PageRank |
1087-4844 | 1 | 0.41 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chan-jong Park | 1 | 53 | 5.76 |
| Il-Kwon Jeong | 2 | 16 | 4.65 |
| Hyeong-Kyo Kim | 3 | 1 | 1.08 |
| KwangYun Wohn | 4 | 309 | 42.24 |