Friday seminar on my department.

1. Particle filters
Maria Mikhisor

2. 3D without glasses

3. Autosterescopic
display
Eye tracking
3D without
glasses

4. Eye tracking
To simplify my task, I started from head
tracking

5. My task is to track faces on
different distances

6. There are many different algorithms for
tracking…
For various reasons I chose Particle Filters

8. Not this kind of
particles…

9. A particle is a hypothesis, one
possible state of a system

11. Prior distribution
Frame 1

12. Prior distribution
Frame 2

13. Prior distribution
Frame 2

14. Observation
Frame 2

15. Averaging
posterior
Frame 2

16. Resampling
Frame 2

17. Prior distribution
Frame 3

18. Move
Frame 3

19. Resample
Frame 3

20. 5 distances
1m – 3m
3 videos for
each
distance
3 experiments
for each video
My experiments

21. Getting ground truth data:
1 video 1 min long labeling manually
1800 frames

22. Motion model
position 1st order
velocity
acceleration
2nd order
3d order

23. Motion model

25. Observation models

26. Color histogram

27. Active shape model

28. Histogram of oriented edges

29. Haar wavelets

30. Combining feature
detectors
Cascades
Linear combinations

31. Color histogram + Haar wavelets

32. Color histogram + Active contour

33. Simple histogram VS compound histogram

34. Ellipse parameterization

35. Number of particles

36. Plans for near future…

37. Cylindrical projection

38. Multi-cameras tracking
One 3D Particle Filter
Two 2D Particle Filters
that communicate or

39. Use Kinect for collecting ground truth data