1. Monocular Model-Based 3D Tracking of Rigid Objects: A Survey 2008. 12. 11. 백운혁 Chapter 2. Mathematical Tools (Bayesian Tracking)

2. 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

3. 2.6.1. Kalman Filtering Time Update (“Predict”) Measurement Update (“Correct”)

4. 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

5. 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

6. 2.6.1. Kalman Filtering Time Update (“Predict”) (1) Compute the kalman gain (1) Project the state ahead (2) Update estimate with measurement (2) Project the error covariance ahead (3) Update the error covariance Initialize Measurement Update (“Correct”) Initial estimates for and

7. 2.6.1. Kalman Filtering 2D Position-Velocity (PV Model)

8. 2.6.1. Kalman Filtering 2D Position-Velocity (PV Model)

9. 2.6.1. Extended Kalman Filtering

10. 2.6 Particle Filters

11. 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

12. 2.6.2. Particle Filters

13. 2.6.2. Particle Filters

14. 2.6.2. Particle Filters

15. Thanks for your attention