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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Ying Kin Yu Siu Hang Or Kin Hong Wong Kai Ki Lee |
| Copyright Year | 2008 |
| Description | Author affiliation: Dept. of Inf. Eng., Chinese Univ. of Hong Kong, Hong Kong (Kai Ki Lee) || Dept. of Comput. Sci. & Eng., Chinese Univ. of Hong Kong, Hong Kong (Siu Hang Or; Kin Hong Wong) |
| Abstract | The novelty of this paper is the introduction of the interacting multiple model probabilistic data association filter (IMMPDAF) to the pose tracking problem. The interacting multiple model (IMM) technique allows the existence of more than one dynamic system in the filtering process and in return leads to improved accuracy and stability even under abrupt motion changes. The probabilistic data association (PDA) framework makes the automatic selection of measurement sets possible, resulting in enhanced robustness to occlusions and moving objects. As the PDA associates stereo correspondences probabilistically, the explicit establishment of stereo matches is not necessary except during initialization, and the point features presence in the outer region of the stereo image pair can be utilized. The performance is demonstrated by applying the pose information to control cameras in a virtual environment. |
| Starting Page | 1 |
| Ending Page | 4 |
| File Size | 891149 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781424421749 |
| ISSN | 10514651 |
| DOI | 10.1109/ICPR.2008.4761477 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2008-12-08 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Motion estimation Motion control Cameras Filters Radar tracking Magnetic force microscopy Personal digital assistants Robustness Data engineering Information filtering |
| Content Type | Text |
| Resource Type | Article |
| Subject | Computer Vision and Pattern Recognition |
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