Name
Abstract | ||
|---|---|---|
In this paper, we use recently developed robust estimation ideas to improve object tracking by a stationary or nonstationary camera. Large uncertainties are always present in vision-based systems, particularly, in relation to the estimation of the initial state as well as the measurement of object motion. The robustness of these systems can be significantly improved by employing a robust extended Kalman filter (REKF). The system performance can also be enhanced by increasing the spatial di- versity in measurements via employing additional cameras for video capture. We compare the performances of various image segmentation techniques in moving-object localization and show that normal-flow-based segmentation yields comparable results to, but requires significantly less time than, optical-flow-based segmentation. We also demonstrate with simulations that dynamic system modeling coupled with the application of an REKF signif- icantly improves the estimation system performance, particularly, when subjected to large uncertainties. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1109/TVT.2007.897202 | IEEE T. Vehicular Technology |
Keywords | Field | DocType |
simulation,kalman filters,spatial diversity,robust estimator,kalman filtering,mathematical models,system performance,optical flow,kalman filter,motion estimation,extended kalman filter,video capture,robustness,dynamic system,robust control,image segmentation,object tracking | Computer vision,Ultrasonic sensor,Extended Kalman filter,Video capture,Computer science,Image segmentation,Robustness (computer science),Kalman filter,Sensor fusion,Real-time computing,Artificial intelligence,Estimation theory | Journal |
Volume | Issue | ISSN |
56 | 4 | 0018-9545 |
Citations | PageRank | References |
4 | 0.52 | 8 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pubudu N. Pathirana | 1 | 520 | 54.96 |
| Allan E. K. Lim | 2 | 4 | 0.52 |
| Andrey V. Savkin | 3 | 1431 | 178.60 |
| Peter D. Hodgson | 4 | 7 | 1.84 |