cvpr2013-Modeling Mutual Visibility Relationship in Pedestri(3)

发表在CVPR13上的行人检测方法

Table1.Overviewofourpedestriandetectionapproach.1.obtainpartdetectionscoresbypartdetector;

2.estimatep(y1|y2=0,x1)in(2)andφ(y;x)in(3)withthedeepmodelinSection4,estimateφ|xp(y;x)in(3)withGMM;3.p(y11,x2)=p(y1,y2=0|x1,x2)+p(y1,y2=1|x1,x2).

modelwasusedforpedestriandetectionin[25,24].The

approachesin[25,24,19]focusedonisolatedobjectsorpedestrians.Thispaperfocusesonco-existingpedestrians,whichhasnotbeenconsideredin[25,19,24].

3.Overviewofourapproach

Inthispaper,wemainlydiscusstheapproachforpair-wisepedestriansandextendittomorepedestriansinSec-tion4.3.Denotethefeaturesofdetectionwindowwndbyvectorx1

1,containingbothappearanceandpositionin-formation.Denotethelabelofwnd1byy1∈{0,1}.Pedestriandetectionwithadiscriminativemodelaimsatobtainingp(y1|xfor1)foreachwindowwndallsizesofwindows.1inaslidingwin-dowmannerWeconsideran-otherdetectionwindowwnd∈{0,1}.Andwehavethe2withfeaturesxfollowingbymarginalizing2andlabelyy22:

p(y

1|x1,x2)=

p(y1,y2|x1,x2)y2=0,1(1)

=p(y1,y2=1|x1,x2)+p(y1,y2=0|x1,x2),Wheny2=0,wehave

p(y1,y2=0|x1,x2)=p(y1|y2=0,x1)p(y2=0),(2)wherep(y1|y2=0,x1)isobtained0)isfromaconstantthedeeppriormodelforisolatedpedestrians.p(ywhich2=onwnd2beingabackground,isobtainedfromcross-validation.Wheny2=1,wehave

p(y1,y2=1|x1,x2)∝φ(y;x)φp(y;x),

(3)

φ(y;x)in(3)isusedforrecognizingpair-wiseco-existing

pedestriansfrompartdetectionscores,wherex=[xT1xT2]T

,y=1ify1=1andy2=1,otherwisey=0.Bothp(ydeep1|ymodel2=0,xintroduced1,x2)andφ(y;x)areobtainedfromtheinSection4.φtherelativepositionp(y;x)in(3)mod-elsprobabilityforbetweenwndwndφmixturemodel1and(GMM).2.p(y;x)isestimatedfromGaussianAnoverviewofourapproachisgiveninTable1.

4.Themutualvisibilitydeepmodel

Sincethevisibilityrelationshipofpartsbetweenpair-wisepedestriansisdifferentwhenpedestrianshavedifferentrelativepositions,therelativepositionsareclusteredintoKmixturesusingGMM.AndKdeepmodelsaretrainedfortheseKmixtures.Apairofdetectionwindowsare

(a)

(b)

Figure2.(a)Themutualvisibilitydeepmodelusedforinferenceand netuningparametersand(b)thedetailedconnectionandpartsmodelforpedestrian1.

classi edintothekthmixtureandthenthispairareusedbythekthdeepmodelforlearningandinference.Thedifferencesbetweenthetwopedestriansinhorizontallo-cation,verticallocationandsize,denotedby(drandomvariablesintheGMMx,ddistribu-y,ds),areusedasthetionp(dx,dy,d.φs).Positivesamplesisobtainedarefromusedp(fordtrainingp(dx,dy,ds)p(y;x)in(3)x,dy,ds).

4.1.Thedeepmodelattheinferencestage

Fig.2(a)showsthedeepmodelusedattheinferencestage.Fig.2(b)showsthepartsmodelusedforpedestrian1atwindowwnddowwnd1.Thepartsmodelforpedestrian2atwin-2isthesame.AsshowninFig.2(b),thereare3layersofpartswithdifferentsizes.Foreachpedestrian,therearesixsmallpartsatlayer1,sevenmedium-sizedpartsatlayer2andsevenlargepartsatLayer3.Thesixpartsatlayer1areleft-head-shoulder,right-head-shoulder,left-torso,right-torso,left-legandright-leg.Apartatanup-perlayerconsistsofitschildrenatthelowerlayer.Thepartsatthetoplayerarethepossibleocclusionstatuseswithgraycolorindicatingocclusions.

ThedetectionscoresforLlayersaredenotedbys=

[s1T

...sLT]T=γ(x),whereγ(x)isobtainedfrompartdetectors,slforl=1,...,Ldenotesthescoresatlayerl.ForthemodelinFig.2,L=3.Andwehave

sl=[slT1slT2]T,wherethePl

scoresofthetwopedestrians

atlayerlaredenotedbysl1=[s11,1,...,slT

1,Pl]andsl2=

[s12,1,...,slT

2,Pl].ThevisibilitiesofPlpartsaredenotedby hl=[h1,...,hll]Tandspectively.11,1h l2=[h12,...,hll]Tre- hl=[h lT1 ThehThidden1,Pvariablesatlayer,1laredenoted2,P

by

l2

]T.Sinceh lisnotprovidedattrainingstage

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