Receding Horizon Estimation for Hybrid Particle Filters and Application for Robust Visual Tracking
The receding horizon estimation is applied to design robust visual trackers. Most recent data within the fixed size of windows is receding, and is processed to obtain an estimate of the object state at the current time. In visual tracking such a scheme improves filter accuracy by avoiding accumulate...
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| creator | Du Yong Kim Ehwa Yang Moongu Jeon Shin, Vladimir |
| description | The receding horizon estimation is applied to design robust visual trackers. Most recent data within the fixed size of windows is receding, and is processed to obtain an estimate of the object state at the current time. In visual tracking such a scheme improves filter accuracy by avoiding accumulated approximation errors. A newly derived unscented Kalman filter (UKF) based on the receding horizon strategy is proposed for determining the importance density of the hybrid particle filter. The importance density derived by the receding horizon-based UKF (RHUKF) provides significantly improved accuracy and performance consistency compared to the unscented particle filter (UPF). Visual tracking examples are subsequently tested to demonstrate the advantages of the filter. |
| doi_str_mv | 10.1109/ICPR.2010.856 |
| format | Conference Proceeding |
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Most recent data within the fixed size of windows is receding, and is processed to obtain an estimate of the object state at the current time. In visual tracking such a scheme improves filter accuracy by avoiding accumulated approximation errors. A newly derived unscented Kalman filter (UKF) based on the receding horizon strategy is proposed for determining the importance density of the hybrid particle filter. The importance density derived by the receding horizon-based UKF (RHUKF) provides significantly improved accuracy and performance consistency compared to the unscented particle filter (UPF). Visual tracking examples are subsequently tested to demonstrate the advantages of the filter.</abstract><pub>IEEE</pub><doi>10.1109/ICPR.2010.856</doi><tpages>5</tpages></addata></record> |
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| ispartof | 2010 20th International Conference on Pattern Recognition, 2010, p.3508-3512 |
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| language | eng |
| recordid | cdi_ieee_primary_5597408 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Filtering theory Kalman filtering Kalman filters Particle filter Particle filters Robustness Tracking Visual tracking Visualization |
| title | Receding Horizon Estimation for Hybrid Particle Filters and Application for Robust Visual Tracking |
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