Visual tracking via incremental Log-Euclidean Riemannian subspace learning

Recently, a novel Log-Euclidean Riemannian metric is proposed for statistics on symmetric positive definite (SPD) matrices. Under this metric, distances and Riemannian means take a much simpler form than the widely used affine-invariant Riemannian metric. Based on the Log-Euclidean Riemannian metric...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:2008 IEEE Conference on Computer Vision and Pattern Recognition s. 1 - 8
Hlavní autoři: Xi Li, Weiming Hu, Zhongfei Zhang, Xiaoqin Zhang, Mingliang Zhu, Jian Cheng
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.06.2008
Témata:
Cameras
Covariance matrix
Inference algorithms
Kernel
Lighting
Particle filters
Particle tracking
Principal component analysis
Robustness
Statistics
ISBN:9781424422425, 1424422426
ISSN:1063-6919, 1063-6919
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Abstract Recently, a novel Log-Euclidean Riemannian metric is proposed for statistics on symmetric positive definite (SPD) matrices. Under this metric, distances and Riemannian means take a much simpler form than the widely used affine-invariant Riemannian metric. Based on the Log-Euclidean Riemannian metric, we develop a tracking framework in this paper. In the framework, the covariance matrices of image features in the five modes are used to represent object appearance. Since a nonsingular covariance matrix is a SPD matrix lying on a connected Riemannian manifold, the Log-Euclidean Riemannian metric is used for statistics on the covariance matrices of image features. Further, we present an effective online Log-Euclidean Riemannian subspace learning algorithm which models the appearance changes of an object by incrementally learning a low-order Log-Euclidean eigenspace representation through adaptively updating the sample mean and eigenbasis. Tracking is then led by the Bayesian state inference framework in which a particle filter is used for propagating sample distributions over the time. Theoretic analysis and experimental evaluations demonstrate the promise and effectiveness of the proposed framework.
AbstractList Recently, a novel Log-Euclidean Riemannian metric is proposed for statistics on symmetric positive definite (SPD) matrices. Under this metric, distances and Riemannian means take a much simpler form than the widely used affine-invariant Riemannian metric. Based on the Log-Euclidean Riemannian metric, we develop a tracking framework in this paper. In the framework, the covariance matrices of image features in the five modes are used to represent object appearance. Since a nonsingular covariance matrix is a SPD matrix lying on a connected Riemannian manifold, the Log-Euclidean Riemannian metric is used for statistics on the covariance matrices of image features. Further, we present an effective online Log-Euclidean Riemannian subspace learning algorithm which models the appearance changes of an object by incrementally learning a low-order Log-Euclidean eigenspace representation through adaptively updating the sample mean and eigenbasis. Tracking is then led by the Bayesian state inference framework in which a particle filter is used for propagating sample distributions over the time. Theoretic analysis and experimental evaluations demonstrate the promise and effectiveness of the proposed framework.
Author Zhongfei Zhang
Mingliang Zhu
Jian Cheng
Xi Li
Xiaoqin Zhang
Weiming Hu
Author_xml – sequence: 1
  surname: Xi Li
  fullname: Xi Li
  organization: Nat. Lab. of Pattern Recognition, Chinese Acad. of Sci., Beijing
– sequence: 2
  surname: Weiming Hu
  fullname: Weiming Hu
  organization: Nat. Lab. of Pattern Recognition, Chinese Acad. of Sci., Beijing
– sequence: 3
  surname: Zhongfei Zhang
  fullname: Zhongfei Zhang
– sequence: 4
  surname: Xiaoqin Zhang
  fullname: Xiaoqin Zhang
  organization: Nat. Lab. of Pattern Recognition, Chinese Acad. of Sci., Beijing
– sequence: 5
  surname: Mingliang Zhu
  fullname: Mingliang Zhu
  organization: Nat. Lab. of Pattern Recognition, Chinese Acad. of Sci., Beijing
– sequence: 6
  surname: Jian Cheng
  fullname: Jian Cheng
  organization: Nat. Lab. of Pattern Recognition, Chinese Acad. of Sci., Beijing
BookMark eNpVkMtOwzAURA0UiVLyAYhNfiDBvrFje4milocigSrotnLsm2JI3CoPJP6eSHQBs5mRjmYWc0lmYR-QkGtGU8aovi02L-sUKFUpF0oKlp-QSEvFOHAOwDM4JXNG8yzJNdNn_xiI2R92QaK-_6CTuMhyls_J08b3o2nioTP204dd_OVN7IPtsMUwTKDc75LlaBvv0IR47bE1Ifgp9mPVH4zFuEHThal6Rc5r0_QYHX1B3lbL1-IhKZ_vH4u7MnkHqodE6Zpa0AKlETqvsVZVVQuF0lkJltKKKaeRM1456kCCME45I2TFETQDzBbk5nfXI-L20PnWdN_b4zPZD-yUVRQ
ContentType Conference Proceeding
DBID 6IE
6IH
CBEJK
RIE
RIO
DOI 10.1109/CVPR.2008.4587516
DatabaseName IEEE Electronic Library (IEL) Conference Proceedings
IEEE Proceedings Order Plan (POP) 1998-present by volume
IEEE Xplore All Conference Proceedings
IEEE Electronic Library (IEL)
IEEE Proceedings Order Plans (POP) 1998-present
DatabaseTitleList
Database_xml – sequence: 1
  dbid: RIE
  name: IEEE Electronic Library (IEL)
  url: https://ieeexplore.ieee.org/
  sourceTypes: Publisher
DeliveryMethod fulltext_linktorsrc
Discipline Applied Sciences
Statistics
Computer Science
EISBN 9781424422432
1424422434
EISSN 1063-6919
EndPage 8
ExternalDocumentID 4587516
Genre orig-research
GroupedDBID 23M
29F
29O
6IE
6IH
6IK
ABDPE
ACGFS
ALMA_UNASSIGNED_HOLDINGS
CBEJK
IPLJI
M43
RIE
RIO
RNS
ID FETCH-LOGICAL-h209t-89f0c295e7a596fef8bbf58e7dc72c00b18d9e414bd0d2725ad8da57b4e2912e3
IEDL.DBID RIE
ISBN 9781424422425
1424422426
ISICitedReferencesCount 56
ISICitedReferencesURI http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000259736801005&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN 1063-6919
IngestDate Wed Aug 27 02:16:47 EDT 2025
IsPeerReviewed false
IsScholarly true
Language English
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-h209t-89f0c295e7a596fef8bbf58e7dc72c00b18d9e414bd0d2725ad8da57b4e2912e3
PageCount 8
ParticipantIDs ieee_primary_4587516
PublicationCentury 2000
PublicationDate 2008-June
PublicationDateYYYYMMDD 2008-06-01
PublicationDate_xml – month: 06
  year: 2008
  text: 2008-June
PublicationDecade 2000
PublicationTitle 2008 IEEE Conference on Computer Vision and Pattern Recognition
PublicationTitleAbbrev CVPR
PublicationYear 2008
Publisher IEEE
Publisher_xml – name: IEEE
SSID ssj0000453616
ssj0023720
ssj0003211698
Score 2.0750794
Snippet Recently, a novel Log-Euclidean Riemannian metric is proposed for statistics on symmetric positive definite (SPD) matrices. Under this metric, distances and...
SourceID ieee
SourceType Publisher
StartPage 1
SubjectTerms Cameras
Covariance matrix
Inference algorithms
Kernel
Lighting
Particle filters
Particle tracking
Principal component analysis
Robustness
Statistics
Title Visual tracking via incremental Log-Euclidean Riemannian subspace learning
URI https://ieeexplore.ieee.org/document/4587516
WOSCitedRecordID wos000259736801005&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
link http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV07T8MwELYoYmAqjyLeysCIwXbi14yoEEKoQoC6VY59hkiQorbp78dO3CIkFjb7MiS6JL7vnh9CF0oUnHuQuAQiceGZxsYyi00ZHFzNLNfUt2QT8vFRjcd6tIEu170wANAWn8FVXLa5fDe1TQyVXRc8oGsqeqgnpeh6tdbxlABNcpGgTtznwbMRep1RYJGNpc18ihwLTfWqyYtFG7Wa_ZT2PKU_KdHXN6-jp67kMt39Fw1La4WG_f89_w4a_LTzZaO1odpFG1DvoX7Cn1n6u-dBtKJ4WMn20HYEo90s5310_1rNG_ORLWbGxgh7tqxMVtW2CzGGCw_TN3zb2I_Kgamzpwo-IyNSWM7D8RScc8gSScXbAL0Mb59v7nDiYsDvjOgFVtoTyzQHabgWHrwqS88VSGcls4SUVDkNBS1KRxyTjBunnOGyLIBpyiA_QJv1tIZDlFHlVaHKAFyILwwxijhLHA2vVIQTT-ZHaD-qbvLVjduYJK0d_y0-QdtdCUcMjJyizcWsgTO0ZZdBObPz9hv5BvFqs6Q
linkProvider IEEE
linkToHtml http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3NTxQxFH9BNJETChhExTl4tNJ2-nkmEJR1syFIuG067StMArNmP_j7aWe6a0y8eGvfHCbz0un7vc8fwBejhJQRNWmQaiIit8R57olrkoNruZeWxZ5sQo_H5vbWTrbg66YXBhH74jP8lpd9Lj_M_CqHyk6ETOiaqRfwUgrB6dCttYmoJHBSqwJ28r5Ovo2ym5wCz3wsfe5T1URZZtdtXjxbqfX0p7KXJQHKqD05vZlcDUWX5f1_EbH0duh89_--4A0c_GnoqyYbU_UWtrDbg92CQKvyfy-SaE3ysJbtwU6Go8M05334cdMuVu6hWs6dzzH26ql1Vdv5IciYHoxmd-Rs5R_agK6rrlp8zJxIablIF1Ryz7EqNBV3B_Dr_Oz69IIUNgZyz6ldEmMj9dxK1E5aFTGaponSoA5ec09pw0ywKJhoAg1cc-mCCU7qRiC3jGP9Dra7WYeHUDETjTBNgi40CkedocHTwLRWKt15un4P-1l109_DwI1p0drRv8Wf4fXF9c_RdPR9fPkBdoaCjhwm-Qjby_kKP8Er_5QUNT_uz8szfwq26w
openUrl ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=proceeding&rft.title=2008+IEEE+Conference+on+Computer+Vision+and+Pattern+Recognition&rft.atitle=Visual+tracking+via+incremental+Log-Euclidean+Riemannian+subspace+learning&rft.au=Xi+Li&rft.au=Weiming+Hu&rft.au=Zhongfei+Zhang&rft.au=Xiaoqin+Zhang&rft.date=2008-06-01&rft.pub=IEEE&rft.isbn=9781424422425&rft.issn=1063-6919&rft.eissn=1063-6919&rft.spage=1&rft.epage=8&rft_id=info:doi/10.1109%2FCVPR.2008.4587516&rft.externalDocID=4587516
thumbnail_l http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1063-6919&client=summon
thumbnail_m http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1063-6919&client=summon
thumbnail_s http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1063-6919&client=summon