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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Kragic, D. Christensen, H.I. |
| Copyright Year | 2002 |
| Description | Author affiliation: Centre for Autonomous Syst., R. Inst. of Technol., Stockholm, Sweden (Kragic, D.; Christensen, H.I.) |
| Abstract | Traditionally, fusion of visual information for tracking has been based on explicit models for uncertainty and integration. Most of the approaches use some form of Bayesian statistics where strong models are employed. We argue that for cases where a large number of visual features are available, weak models for integration may be employed. We analyze integration by voting where two methods are proposed and evaluated: (i) response and (ii) action fusion. The methods differ in the choice of voting space: the former integrates visual information in image space and latter in velocity space. We also evaluate four weighting techniques for integration. |
| Sponsorship | IEEE Robotics & Autom. Soc |
| Starting Page | 3044 |
| Ending Page | 3049 |
| File Size | 655720 |
| Page Count | 6 |
| File Format | |
| ISBN | 0780372727 |
| DOI | 10.1109/ROBOT.2002.1013694 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2002-05-11 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Voting Bayesian methods Uncertainty Robustness Numerical analysis Computer science Statistics Focusing Tracking loops Acceleration |
| Content Type | Text |
| Resource Type | Article |
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