1 / 30

Tracking through Optical Snow

Tracking through Optical Snow. Michael Langer Richard Mann School of Computer Science School of Computer Science McGill U. U. Waterloo. Optical snow. e.g. falling snow. Optical snow. Optical snow.

zia
Download Presentation

Tracking through Optical Snow

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Tracking through Optical Snow Michael Langer Richard Mann School of Computer Science School of Computer Science McGill U. U. Waterloo

  2. Optical snow e.g. falling snow

  3. Optical snow

  4. Optical snow Moving observer in a 3D cluttered scene

  5. Optical snow

  6. Related work Computation of Image Flow: “The Fox and the Forest” (Steve Zucker ’80’s) Psychophysics of Heading: “3D cloud of dots” (Bill Warren ’80s-’90s) Ecological Optics (J. J. Gibson) monkeys in a forest, cats in the tall grass. But how ?

  7. Goal of this talk How to model and compute image velocities in a 3D cluttered scene ?

  8. Overview of Talk • Fourier analysis of optical snow(Langer & Mann, ICCV ’01) • Generalized optical snow • Biologically motivated computational model (sketch only)

  9. Fourier model of image translation (Fahle & Poggio ’81, Watson & Ahumada ’85) f t t f y f x v f + v f + f = 0 x x y y t

  10. Optical Snow (v , v ) = (αt , αt ) x y x y

  11. Optical Snow (v , v ) = (αt , αt ) x y x y

  12. Optical snow v y v x (v , v ) = (αt , αt ) x y x y

  13. Fourier model of optical snow “bowtie” αt f +αt f + f = 0 x x y y t

  14. Example of bowtie in power spectrum bush sequence

  15. Overview of Talk • Fourier analysis of optical snow(Langer & Mann, ICCV ’01) • Generalized optical snow • Biologically motivated computational model (sketch only)

  16. Moving observer in 3D cluttered scene rotation translation

  17. Moving observer in 3D cluttered scene (Longuet-Higgins and Prazdney 1980) • Velocity field is the sum of two fields : • translation of camera • - depends on 3D scene geometry (depth) • rotation of camera • - independent of 3D scene geometry

  18. Moving observer in 3D cluttered scene rotation(pan + tilt) translation(lateral)

  19. vertical translation + pan to left

  20. Tracking through optical snow vertical translation + pan to left

  21. Generalized optical snow v y v x (v , v ) =( αt ,αt )+(w , w) x y x y x y translation rotation

  22. Fourier model of generalized optical snow “tilted bowtie” (αt+ w ) f + (αt+ w ) f + f = 0 xx x yy y t

  23. Tilted bowtie in power spectrum vertical translation + pan to left

  24. Overview of Talk • Fourier analysis of optical snow • Generalized optical snow • Biologically motivated computational model (sketch only)

  25. Oriented, directionally tuned cells in V1. f t - f - y + - - + - + f - x

  26. f t f y f x Oriented, directionally tuned cells in V1.

  27. f t f y f x Pure image translation (v , v ) x y f t t f y f x (see Heeger ’87, Yuille and Grzywacz ’90, Simoncelli and Heeger ‘97)

  28. Generalized optical snow f t f x f y (Langer and Mann, in preparation)

  29. Summary • Goal: how to model and compute image velocities in a 3D cluttered scene ? • Generalized optical snow: lateral motion + pan and tilt → tilted bowtie in frequency domain. • Many algorithms possible for fitting bowtie

  30. Computational models of heading - Longuet-Higgins and Prazdney 1980 • Rieger and Lawton 1994 • Heeger and Jepson 1992 • Hildreth 1992, • Lappe and Rauschecker 1993 • Royden 1997, …. These models assume “the image velocity field” can be pre-computed. But this assumption is problematic in a 3D cluttered scene.

More Related