IEEE Trans Image Process - Robust online multiobject tracking with data association and track management.

Tópicos

{ motion(1329) object(1292) video(1091) }
{ perform(999) metric(946) measur(919) }
{ extract(1171) text(1153) clinic(932) }
{ analysi(2126) use(1163) compon(1037) }
{ data(1737) use(1416) pattern(1282) }
{ risk(3053) factor(974) diseas(938) }
{ first(2504) two(1366) second(1323) }
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{ state(1844) use(1261) util(961) }
{ patient(2837) hospit(1953) medic(668) }
{ data(2317) use(1299) case(1017) }
{ age(1611) year(1155) adult(843) }
{ medic(1828) order(1363) alert(1069) }
{ signal(2180) analysi(812) frequenc(800) }
{ cost(1906) reduc(1198) effect(832) }
{ group(2977) signific(1463) compar(1072) }
{ sampl(1606) size(1419) use(1276) }
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{ survey(1388) particip(1329) question(1065) }
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{ decis(3086) make(1611) patient(1517) }
{ activ(1452) weight(1219) physic(1104) }
{ method(2212) result(1239) propos(1039) }

Resumo

In this paper, we consider a multiobject tracking problem in complex scenes. Unlike batch tracking systems using detections of the entire sequence, we propose a novel online multiobject tracking system in order to build tracks sequentially using online provided detections. To track objects robustly even under frequent occlusions, the proposed system consists of three main parts: 1) visual tracking with a novel data association with a track existence probability by associating online detections with the corresponding tracks under partial occlusions; 2) track management to associate terminated tracks for linking tracks fragmented by long-term occlusions; and 3) online model learning to generate discriminative appearance models for successful associations in other two parts. Experimental results using challenging public data sets show the obvious performance improvement of the proposed system, compared with other state-of-the-art tracking systems. Furthermore, extensive performance analysis of the three main parts demonstrates effects and usefulness of the each component for multiobject tracking.

Resumo Limpo

paper consid multiobject track problem complex scene unlik batch track system use detect entir sequenc propos novel onlin multiobject track system order build track sequenti use onlin provid detect track object robust even frequent occlus propos system consist three main part visual track novel data associ track exist probabl associ onlin detect correspond track partial occlus track manag associ termin track link track fragment longterm occlus onlin model learn generat discrimin appear model success associ two part experiment result use challeng public data set show obvious perform improv propos system compar stateoftheart track system furthermor extens perform analysi three main part demonstr effect use compon multiobject track

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