Name
Abstract | ||
|---|---|---|
Presents a new framework for combining maximum likelihood (ML) stereo-motion fusion with adaptive iterated extended Kalman filtering (IEKF) for 3-D motion tracking. The ML stereo-fusion step, with two stereo-pairs, generates observations of 3-D feature matches to be used by the IEKF step. The IEKF step, in turn, computes updated 3-D motion parameter estimates to be used by the ML stereo-motion fusion step. The covariance of the observation noise process is regulated by the value of the ML cost function to address occlusion related problems. The proposed simultaneous approach is compared with performing the 3-D feature correspondence estimation and the Kalman filtering separately using simulated stereo imagery |
Year | DOI | Venue |
|---|---|---|
1995 | 10.1109/ICASSP.1995.479945 | Acoustics, Speech, and Signal Processing, 1995. ICASSP-95., 1995 International Conference |
Keywords | Field | DocType |
adaptive Kalman filters,covariance analysis,feature extraction,image matching,iterative methods,maximum likelihood estimation,motion estimation,sensor fusion,stereo image processing,tracking,3-D feature matches,3-D motion tracking,adaptive iterated extended Kalman filtering,cost function,covariance,feature correspondence estimation,maximum likelihood,motion parameter estimates,observation noise,occlusion related problems,stereo-motion fusion,stereo-pairs | Computer vision,Pattern recognition,Computer science,Filter (signal processing),Feature extraction,Kalman filter,Sensor fusion,Adaptive filter,Artificial intelligence,Motion estimation,Estimation theory,Match moving | Conference |
Volume | ISSN | ISBN |
4 | 1520-6149 | 0-7803-2431-5 |
Citations | PageRank | References |
4 | 0.60 | 4 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Y. Altunbasak | 1 | 1042 | 85.73 |
| A. Murat Tekalp | 2 | 2603 | 278.04 |
| G. Bozdagi | 3 | 221 | 21.93 |