Name
Abstract | ||
|---|---|---|
Tracking moving objects is one of the most important techniques in motion analysis and understanding, and it has many difficult problems to solve. Estimating and identifying moving objects, when the background and moving objects vary dynamically, are especially difficult. It is possible under such a complex environment that targets might disappear totally or partially due to occlusion by other objects. The Kalman filter has been used to estimate motion information and use the information in predicting the appearance of targets in succeeding frames. In this article, we propose another version of the Kalman filter, to be called the structural Kalman filter, which can successfully accomplish its role of estimating motion information under such a deteriorating condition as occlusion. Experimental results show that the suggested approach is very effective in estimating and tracking non-rigid moving objects reliably. (C) 2002 Wiley Periodicals, Inc. |
Year | DOI | Venue |
|---|---|---|
2002 | 10.1002/int.10040 | INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS |
Keywords | Field | DocType |
kalman filter | Computer vision,Occultation,Kalman filter,Moving horizon estimation,Artificial intelligence,Motion estimation,Motion analysis,Mathematics | Journal |
Volume | Issue | ISSN |
17 | 6 | 0884-8173 |
Citations | PageRank | References |
0 | 0.34 | 5 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dae-sik Jang | 1 | 84 | 10.99 |
| Seok-Woo Jang | 2 | 55 | 12.72 |
| Hyung-Il Choi | 3 | 138 | 26.28 |