Learning structured visual dictionary for object tracking

Fan Yang, Huchuan Lu, Ming Hsuan Yang

Research output: Contribution to journalArticle

21 Citations (Scopus)

Abstract

In this paper, we propose a visual tracking algorithm by incorporating the appearance information gathered from two collaborative feature sets and exploiting its geometric structures. A structured visual dictionary (SVD) can be learned from both appearance and geometric structure, thereby enhancing its discriminative strength between the foreground object and the background. Experimental results show that the proposed tracking algorithm using SVD (SVDTrack) performs favorably against the state-of-the-art methods.

Original languageEnglish
Pages (from-to)992-999
Number of pages8
JournalImage and Vision Computing
Volume31
Issue number12
DOIs
Publication statusPublished - 2013 Jan 1

Fingerprint

Glossaries

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Computer Vision and Pattern Recognition

Cite this

Yang, Fan ; Lu, Huchuan ; Yang, Ming Hsuan. / Learning structured visual dictionary for object tracking. In: Image and Vision Computing. 2013 ; Vol. 31, No. 12. pp. 992-999.
@article{154895267b7447baa16de98731ec4c77,
title = "Learning structured visual dictionary for object tracking",
abstract = "In this paper, we propose a visual tracking algorithm by incorporating the appearance information gathered from two collaborative feature sets and exploiting its geometric structures. A structured visual dictionary (SVD) can be learned from both appearance and geometric structure, thereby enhancing its discriminative strength between the foreground object and the background. Experimental results show that the proposed tracking algorithm using SVD (SVDTrack) performs favorably against the state-of-the-art methods.",
author = "Fan Yang and Huchuan Lu and Yang, {Ming Hsuan}",
year = "2013",
month = "1",
day = "1",
doi = "10.1016/j.imavis.2013.09.008",
language = "English",
volume = "31",
pages = "992--999",
journal = "Image and Vision Computing",
issn = "0262-8856",
publisher = "Elsevier Limited",
number = "12",

}

Learning structured visual dictionary for object tracking. / Yang, Fan; Lu, Huchuan; Yang, Ming Hsuan.

In: Image and Vision Computing, Vol. 31, No. 12, 01.01.2013, p. 992-999.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Learning structured visual dictionary for object tracking

AU - Yang, Fan

AU - Lu, Huchuan

AU - Yang, Ming Hsuan

PY - 2013/1/1

Y1 - 2013/1/1

N2 - In this paper, we propose a visual tracking algorithm by incorporating the appearance information gathered from two collaborative feature sets and exploiting its geometric structures. A structured visual dictionary (SVD) can be learned from both appearance and geometric structure, thereby enhancing its discriminative strength between the foreground object and the background. Experimental results show that the proposed tracking algorithm using SVD (SVDTrack) performs favorably against the state-of-the-art methods.

AB - In this paper, we propose a visual tracking algorithm by incorporating the appearance information gathered from two collaborative feature sets and exploiting its geometric structures. A structured visual dictionary (SVD) can be learned from both appearance and geometric structure, thereby enhancing its discriminative strength between the foreground object and the background. Experimental results show that the proposed tracking algorithm using SVD (SVDTrack) performs favorably against the state-of-the-art methods.

UR - http://www.scopus.com/inward/record.url?scp=84888112703&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84888112703&partnerID=8YFLogxK

U2 - 10.1016/j.imavis.2013.09.008

DO - 10.1016/j.imavis.2013.09.008

M3 - Article

AN - SCOPUS:84888112703

VL - 31

SP - 992

EP - 999

JO - Image and Vision Computing

JF - Image and Vision Computing

SN - 0262-8856

IS - 12

ER -