IEEE Trans Image Process - Real-time probabilistic covariance tracking with efficient model update.

Tópicos

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{ motion(1329) object(1292) video(1091) }
{ perform(1367) use(1326) method(1137) }
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{ perform(999) metric(946) measur(919) }
{ import(1318) role(1303) understand(862) }
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{ measur(2081) correl(1212) valu(896) }
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{ 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) }
{ sampl(1606) size(1419) use(1276) }
{ gene(2352) biolog(1181) express(1162) }
{ first(2504) two(1366) second(1323) }
{ activ(1138) subject(705) human(624) }
{ time(1939) patient(1703) rate(768) }
{ patient(1821) servic(1111) care(1106) }
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{ can(981) present(881) function(850) }
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Resumo

The recently proposed covariance region descriptor has been proven robust and versatile for a modest computational cost. The covariance matrix enables efficient fusion of different types of features, where the spatial and statistical properties, as well as their correlation, are characterized. The similarity between two covariance descriptors is measured on Riemannian manifolds. Based on the same metric but with a probabilistic framework, we propose a novel tracking approach on Riemannian manifolds with a novel incremental covariance tensor learning (ICTL). To address the appearance variations, ICTL incrementally learns a low-dimensional covariance tensor representation and efficiently adapts online to appearance changes of the target with only O(1) computational complexity, resulting in a real-time performance. The covariance-based representation and the ICTL are then combined with the particle filter framework to allow better handling of background clutter, as well as the temporary occlusions. We test the proposed probabilistic ICTL tracker on numerous benchmark sequences involving different types of challenges including occlusions and variations in illumination, scale, and pose. The proposed approach demonstrates excellent real-time performance, both qualitatively and quantitatively, in comparison with several previously proposed trackers.

Resumo Limpo

recent propos covari region descriptor proven robust versatil modest comput cost covari matrix enabl effici fusion differ type featur spatial statist properti well correl character similar two covari descriptor measur riemannian manifold base metric probabilist framework propos novel track approach riemannian manifold novel increment covari tensor learn ictl address appear variat ictl increment learn lowdimension covari tensor represent effici adapt onlin appear chang target o comput complex result realtim perform covariancebas represent ictl combin particl filter framework allow better handl background clutter well temporari occlus test propos probabilist ictl tracker numer benchmark sequenc involv differ type challeng includ occlus variat illumin scale pose propos approach demonstr excel realtim perform qualit quantit comparison sever previous propos tracker

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