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# 3d tracking : chapter2-2 kalman filter

## by Woonhyuk Baek, 팀원 at 다음커뮤니케이션 on Jun 30, 2010

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## 3d tracking : chapter2-2 kalman filterPresentation Transcript

• Monocular Model-Based 3D Tracking of Rigid Objects: A Survey
2008. 12. 11.
백운혁
Chapter 2. Mathematical Tools (Bayesian Tracking)
• 2.6 Kalman Filtering
The kalman filter is the best possible (optimal) estimator for a large class of problems and a very effective and useful estimator for an even larger class
• 2.6.1. Kalman Filtering
Time Update
(“Predict”)
Measurement Update
(“Correct”)
• Discrete kalman filter time update equations
project the state and covariance estimates forward from time step to step .
2.6.1. Kalman Filtering
Measurements are derived from the internal state
New state is modeled as a linear combination of both the previous state and som noise
uncertainty
state transition
actual state
estimate state
noise
posteriori estimate error covariance
priori estimate error covariance
• Discrete kalman filter measurement update equations
the next step is to actually measure the process to obtain ,and then to generate an a posteriori state estimate.
2.6.1. Kalman Filtering
the actual measurement
gain or blending factor
measurement matrix
predicted measurement
• 2.6.1. Kalman Filtering
Time Update (“Predict”)
(1) Compute the kalman gain
(2) Update estimate with measurement
(2) Project the error covariance ahead
(3) Update the error covariance
Initialize
Measurement Update (“Correct”)
Initial estimates for and
• 2.6.1. Kalman Filtering
2D Position-Velocity (PV Model)
• 2.6.1. Kalman Filtering
2D Position-Velocity (PV Model)
• 2.6.1. Extended Kalman Filtering
• 2.6 Particle Filters
• 2.6.2. Particle Filters
general representation by a set of weighted hypotheses, or particles
do not require the linearization of the relation between the state and the measurements
gives increased robustness
but few papers on particle based 3D pose estimation
• 2.6.2. Particle Filters
• 2.6.2. Particle Filters
• 2.6.2. Particle Filters