Object tracking using particle filter презентация
Содержание
- 2. Overview Background Information Basic Particle Filter Theory Rao Blackwellised Particle Filter
- 3. Object Tracking Tracking objects in video involves the modeling of non-linear
- 4. Background In order to model accurately the underlying dynamics of a
- 5. The Particle Filter Particle Filter is concerned with the problem of
- 6. Mathematical Background Particle Filtering estimates the state of the system, x
- 7. Mathematical Background Particle filtering assumes a Markov Model for system state
- 8. Mathematical Background Est(t) = P( x t | y 0 -
- 9. Mathematical Background Final Result: Est(t) = p(y t | x t
- 10. Mathematical Background To implement Particle Filter we need State Motion model:
- 11. Mathematical Background We sample from the proposal and not the posterior
- 12. Basic Particle Filter Theory A discrete set of samples or particles
- 13. Basic Particle Filter Theory (Cont.) Particle Filter is concerned with the
- 14. Basic Particle Filter Theory (Cont.) System Dynamics ie.Motion Model: p(x t|
- 15. Basic Particle Filter Theory (Cont.) Given N particles (samples)
- 16. Basic Particle Filter Theory (Cont.) The basic Particle Filter algorithm consists
- 17. Particle Filter Algorithm Sequential importance sampling Uses Sequential Monte Carlo simulation.
- 18. Particle Filter Algorithm Selection Step Multiply or discard particles with respect
- 19. Rao-Blackwellised Particle Filter RBPF is an extension on PF. It uses
- 20. RBPF Approach RBPF models the states as <Ct,Dt> Ct is the
- 21. Implementation We have implemented the Particle Filter algorithm in Matlab. Our
- 22. Implementation Color Based Probabilistic Tracking These trackers rely on the deterministic
- 23. Color Based Probabilistic Tracking The combination of tools used to accomplish
- 24. Color Based Probabilistic Tracking Reference Color Window The target object to
- 25. Color Based Probabilistic Tracking State Space We have modeled the states,
- 26. Color Based Probabilistic Tracking System Dynamics A second-order auto-regressive dynamics is
- 27. Color Based Probabilistic Tracking Observation yt The observation yt is proportional
- 28. Color Based Probabilistic Tracking Particle Filter Iteration Steps: Initialize xt
- 29. Color Based Probabilistic Tracking An step by step look at our
- 30. Color Based Probabilistic Tracking For each particle, we apply the second
- 31. Color Based Probabilistic Tracking Calculate the histogram distance: for k =
- 32. Color Based Probabilistic Tracking Re-sampling step, where the new particle set
- 33. Color Based Probabilistic Tracking Functions Used: Track_final1.m : PF tracking code
- 34. Color Based Probabilistic Tracking: Results
- 35. Applications Video Surveillance Gesture HCI Reality and Visual Effects Medical
- 36. Future Work Automatic initialization of reference window. Multi part color window.
- 37. References M. Isard and A. Blake. Condensation–conditional density propagation for visual
- 38. Thank You
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