Background subtraction is the first step of many video surveillance applications. What is considered background varies by application, and may include regular, systematic, or complex motions. The paper explores the use of several different local spatio-temporal models of a background, defined at each pixel in the image. We present experiments with real image data and conclude that appropriate local representations are sufficient to make background models of complicated real world motions. Empirical studies illustrate, for example, that an optical flow-based model is able to detect emergency vehicles whose motion is different from those typically observed in traffic scenes. We conclude that "different models are appropriate for different scenes", but give criteria by which one can choose which model will be best.

Whru a dilt~lhSC is replicated at, many sites2 maintaining mutual consistrnry among t,he sites iu the fac:e of updat,es is a signitirant problem.This paper descrikrs several randomized algorit,hms for dist,rihut.ingupdates and driving t,he replicas toward consist,c>nc,y.The algorit Inns are very simple and require few guarant,ees from the underlying conllllunicat.iollsystem, yc+ they rnsutc t.hat.the off(~c~t, of ('very update is evcnt,uwlly rf+irt-ted in a11 rq1ica.s.The cost, and parformancc of t,hr algorithms arc tuned I>? c%oosing appropriat,c dist,rilMions in t,hc randoinizat,ioii step.TIN> idgoritlmls ilr(' c*los~*ly analogoIls t,o epidemics, and t,he epi-dcWliolog)-litc\ratiirc, ilitlh iii Illld~~rsti4lldill~ tlicir bc*liavior.One of tlW i$,oritlims 11&S brc>n implrmcWrd in the Clraringhousr sprv(brs of thr Xerox C'orporat~c~ Iiitcrnc4, solviiig long-standing prol>lf~lns of high traffic and tlatirl>ilsr inconsistcllcp.

No abstract available.