Motion estimation. Announcements. Outline. Motion estimation

Transcription

1 Annoncemens Moion esimaion Projec # is e on ne Tesa sbmission mechanism will be annonce laer his week. graing: reor is imoran resls goo/ba iscssions on imlemenaion inerface feares ec. Digial Visal Effecs Sring 5 Yng-Y Chang 5/3/3 wih slies b Michael Black an P. Ananan Oline Moion esimaion Lcas-Kanae algorihm Tracking Oical flow Moion esimaion Parameric moion image alignmen Tracking Oical flow

2 Parameric moion Tracking Oical flow Three assmions Brighness consisenc Saial coherence Temoral ersisence

3 Brighness consisenc Saial coherence Temoral ersisence mage regisraion Goal: regiser a emlae image an an in image where = T. mage alignmen: an are wo images Tracking: is he image a ime. is a small ach aron he oin in he image a +. Oical flow: an are images of an +.

4 Simle aroach Minimize brighness ifference E Simle SSD algorihm For each offse come E; Choose which minimizes E; Problems: No efficien No sb-iel accrac Newon s meho Roo fining for f= Talor s eansion: Lcas-Kanae algorihm

5 Lcas-Kanae algorihm E E E Lcas-Kanae algorihm E E Lcas-Kanae algorihm ierae shif wih come graien image come error image - come Hessian mari sole he linear ssem =+ nil conerge Parameric moel E ; E T ; ranslaion T A ; affine

6 Parameric moel ; minimize wih resec o ; ; ; ; ; ; minimize Parameric moel ; image graien acobian of he war ware image n n acobian of he war For eamle for affine ; n n Parameric moel ; minimize ; T ; H T H T Hessian

7 Lcas-Kanae algorihm ierae war wih ; come error image - come graien image ealae acobian a ; come come Hessian come sole ae b + nil conerge T ; H ; T Coarse-o-fine sraeg Alicaion of image alignmen rami consrcion a in war w refine + a war w refine + a a rami consrcion war w refine + a a o

8 Tracking Tracking Tracking oical flow consrain eqaion brighness consanc Oical flow consrain eqaion

9 Mlile consrain Area-base meho Assme saial smoohness Aerre roblem Aerre roblem

10 Aerre roblem Demo for aerre roblem h:// eahing_objecs.hm h:// rberole.hm Aerre roblem Larger winow reces ambigi b easil iolaes saial smoohness assmion Area-base meho Assme saial smoohness E

11 Area-base meho Area-base meho The eigenales ell s abo he local image srcre. The also ell s how well we can esimae he flow in boh irecions Link o Harris corner eecor ms be inerible Tere area Ege

12 Homogenos area KLT racking Selec feare b min Monior feares b measring issimilari

13 KLT racking h:// KLT racking SFT racking maching acall Frame Frame h://

14 SFT racking SFT racking Frame Frame Frame Frame KLT s SFT racking Tracking for rooscoing KLT has larger accmlaing error; arl becase or KLT imlemenaion oesn hae affine ransformaion? SFT is srrisingl robs

15 Tracking for rooscoing aking life Single-moion assmion Oical flow Violae b Moion isconini Shaows Transarenc Seclar reflecion

16 Mlile moion Mlile moion Simle roblem: fi a line Leas-sqare fi

17 Leas-sqare fi Robs saisics Recoer he bes fi for he majori of he aa Deec an rejec oliers Aroach Robs weighing

18 Robs esimaion

19 Reglarizaion an ense oical flow

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22 n for he NPR algorihm Brshes Ege cliing Graien

23 Smooh graien Tere brsh Ege cliing Temoral arifacs Frame-b-frame alicaion of he NPR algorihm

24 Temoral coherence RE:Vision ha reams ma come Reference B.D. Lcas an T. Kanae An eraie mage Regisraion Techniqe wih an Alicaion o Sereo Vision Proceeings of he 98 DARPA mage Unersaning orksho Bergen. R. an Ananan P. an Hanna K.. an Hingorani R. Hierarchical Moel-Base Moion Esimaion ECCV Shi an C. Tomasi Goo Feares o Track CVPR Michael Black an P. Ananan The Robs Esimaion of Mlile Moions: Parameric an Piecewise-Smooh Flow Fiels Comer Vision an mage Unersaning S. Baker an. Mahews Lcas-Kanae Years On: A Unifing Framework nernaional ornal of Comer Vision Peer Liwinowicz Processing mages an Vieo for An mressionis Effecs SGGRAPH 997. Aseem Agarwala Aaron Herzman Dai Salesin an Seen Seiz Keframe-Base Tracking for Rooscoing an Animaion SGGRAPH