IEEE Trans Image Process - Efficient object tracking by incremental self-tuning particle filtering on the affine group.

Tópicos

{ state(1844) use(1261) util(961) }
{ visual(1396) interact(850) tool(830) }
{ motion(1329) object(1292) video(1091) }
{ sampl(1606) size(1419) use(1276) }
{ data(1737) use(1416) pattern(1282) }
{ method(1219) similar(1157) match(930) }
{ imag(2830) propos(1344) filter(1198) }
{ problem(2511) optim(1539) algorithm(950) }
{ health(1844) social(1437) communiti(874) }
{ can(774) often(719) complex(702) }
{ assess(1506) score(1403) qualiti(1306) }
{ framework(1458) process(801) describ(734) }
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{ model(3404) distribut(989) bayesian(671) }
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{ bind(1733) structur(1185) ligand(1036) }
{ structur(1116) can(940) graph(676) }
{ imag(1947) propos(1133) code(1026) }
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{ imag(2675) segment(2577) method(1081) }
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{ research(1218) medic(880) student(794) }
{ patient(2837) hospit(1953) medic(668) }
{ model(2656) set(1616) predict(1553) }
{ 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) }
{ gene(2352) biolog(1181) express(1162) }
{ data(3008) multipl(1320) sourc(1022) }
{ first(2504) two(1366) second(1323) }
{ intervent(3218) particip(2042) group(1664) }
{ time(1939) patient(1703) rate(768) }
{ patient(1821) servic(1111) care(1106) }
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{ detect(2391) sensit(1101) algorithm(908) }

Resumo

We propose an incremental self-tuning particle filtering (ISPF) framework for visual tracking on the affine group, which can find the optimal state in a chainlike way with a very small number of particles. Unlike traditional particle filtering, which only relies on random sampling for state optimization, ISPF incrementally draws particles and utilizes an online-learned pose estimator (PE) to iteratively tune them to their neighboring best states according to some feedback appearance-similarity scores. Sampling is terminated if the maximum similarity of all tuned particles satisfies a target-patch similarity distribution modeled online or if the permitted maximum number of particles is reached. With the help of the learned PE and some appearance-similarity feedback scores, particles in ISPF become "smart" and can automatically move toward the correct directions; thus, sparse sampling is possible. The optimal state can be efficiently found in a step-by-step way in which some particles serve as bridge nodes to help others to reach the optimal state. In addition to the single-target scenario, the "smart" particle idea is also extended into a multitarget tracking problem. Experimental results demonstrate that our ISPF can achieve great robustness and very high accuracy with only a very small number of particles.

Resumo Limpo

propos increment selftun particl filter ispf framework visual track affin group can find optim state chainlik way small number particl unlik tradit particl filter reli random sampl state optim ispf increment draw particl util onlinelearn pose estim pe iter tune neighbor best state accord feedback appearancesimilar score sampl termin maximum similar tune particl satisfi targetpatch similar distribut model onlin permit maximum number particl reach help learn pe appearancesimilar feedback score particl ispf becom smart can automat move toward correct direct thus spars sampl possibl optim state can effici found stepbystep way particl serv bridg node help other reach optim state addit singletarget scenario smart particl idea also extend multitarget track problem experiment result demonstr ispf can achiev great robust high accuraci small number particl

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