IEEE Trans Image Process - 3-D target-based distributed smart camera network localization.

Tópicos

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{ imag(1057) registr(996) error(939) }
{ detect(2391) sensit(1101) algorithm(908) }
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{ high(1669) rate(1365) level(1280) }
{ can(774) often(719) complex(702) }
{ take(945) account(800) differ(722) }
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{ monitor(1329) mobil(1314) devic(1160) }
{ ehr(2073) health(1662) electron(1139) }
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{ activ(1452) weight(1219) physic(1104) }
{ method(2212) result(1239) propos(1039) }

Resumo

For distributed smart camera networks to perform vision-based tasks such as subject recognition and tracking, every camera's position and orientation relative to a single 3-D coordinate frame must be accurately determined. In this paper, we present a new camera network localization solution that requires successively showing a 3-D feature point-rich target to all cameras, then using the known geometry of a 3-D target, cameras estimate and decompose projection matrices to compute their position and orientation relative to the coordinatization of the 3-D target's feature points. As each 3-D target position establishes a distinct coordinate frame, cameras that view more than one 3-D target position compute translations and rotations relating different positions' coordinate frames and share the transform data with neighbors to facilitate realignment of all cameras to a single coordinate frame. Compared to other localization solutions that use opportunistically found visual data, our solution is more suitable to battery-powered, processing-constrained camera networks because it requires communication only to determine simultaneous target viewings and for passing transform data. Additionally, our solution requires only pairwise view overlaps of sufficient size to see the 3-D target and detect its feature points, while also giving camera positions in meaningful units. We evaluate our algorithm in both real and simulated smart camera networks. In the real network, position error is less than 1 ('') when the 3-D target's feature points fill only 2.9% of the frame area.

Resumo Limpo

distribut smart camera network perform visionbas task subject recognit track everi camera posit orient relat singl d coordin frame must accur determin paper present new camera network local solut requir success show d featur pointrich target camera use known geometri d target camera estim decompos project matric comput posit orient relat coordinat d target featur point d target posit establish distinct coordin frame camera view one d target posit comput translat rotat relat differ posit coordin frame share transform data neighbor facilit realign camera singl coordin frame compar local solut use opportunist found visual data solut suitabl batterypow processingconstrain camera network requir communic determin simultan target view pass transform data addit solut requir pairwis view overlap suffici size see d target detect featur point also give camera posit meaning unit evalu algorithm real simul smart camera network real network posit error less d target featur point fill frame area

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