Robust visual tracking using local sparse covariance descriptor and matching pursuit

Bo Ma; Hongwei Hu; Shiqi Liu; Jianglong Chen

doi:10.1007/978-3-642-42051-1_60

Robust visual tracking using local sparse covariance descriptor and matching pursuit

Bo Ma, Hongwei Hu, Shiqi Liu, Jianglong Chen

School of Computer Science and Technology

Beijing Institute of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Citations (Scopus)

Abstract

In this paper, we propose a visual tracking method based on local sparse covariance descriptor and matching pursuit. Covariance descriptor can model feature correlation of target templates effectively, and matching pursuit is employed to select the best target candidate which is reconstructed by target templates. The selection process is performed by solving a least square problem, and the candidate with the smallest projection error is taken as the tracking target. Experimental results on several video sequences demonstrate the good performance of proposed method compared with three existing tracking algorithms.

Original language	English
Title of host publication	Neural Information Processing - 20th International Conference, ICONIP 2013, Proceedings
Pages	485-492
Number of pages	8
Edition	PART 3
DOIs	https://doi.org/10.1007/978-3-642-42051-1_60
Publication status	Published - 2013
Event	20th International Conference on Neural Information Processing, ICONIP 2013 - Daegu, Korea, Republic of Duration: 3 Nov 2013 → 7 Nov 2013

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 3
Volume	8228 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	20th International Conference on Neural Information Processing, ICONIP 2013
Country/Territory	Korea, Republic of
City	Daegu
Period	3/11/13 → 7/11/13

Keywords

Covariance descriptor
Local sparse descriptor
Matching pursuit
Visual tracking

Access to Document

10.1007/978-3-642-42051-1_60

Cite this

Ma, B., Hu, H., Liu, S., & Chen, J. (2013). Robust visual tracking using local sparse covariance descriptor and matching pursuit. In Neural Information Processing - 20th International Conference, ICONIP 2013, Proceedings (PART 3 ed., pp. 485-492). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8228 LNCS, No. PART 3). https://doi.org/10.1007/978-3-642-42051-1_60

Ma, Bo ; Hu, Hongwei ; Liu, Shiqi et al. / Robust visual tracking using local sparse covariance descriptor and matching pursuit. Neural Information Processing - 20th International Conference, ICONIP 2013, Proceedings. PART 3. ed. 2013. pp. 485-492 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 3).

@inproceedings{d1ba6e6ff43f427a9f3c937827c6d47d,

title = "Robust visual tracking using local sparse covariance descriptor and matching pursuit",

abstract = "In this paper, we propose a visual tracking method based on local sparse covariance descriptor and matching pursuit. Covariance descriptor can model feature correlation of target templates effectively, and matching pursuit is employed to select the best target candidate which is reconstructed by target templates. The selection process is performed by solving a least square problem, and the candidate with the smallest projection error is taken as the tracking target. Experimental results on several video sequences demonstrate the good performance of proposed method compared with three existing tracking algorithms.",

keywords = "Covariance descriptor, Local sparse descriptor, Matching pursuit, Visual tracking",

author = "Bo Ma and Hongwei Hu and Shiqi Liu and Jianglong Chen",

year = "2013",

doi = "10.1007/978-3-642-42051-1_60",

language = "English",

isbn = "9783642420504",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

number = "PART 3",

pages = "485--492",

booktitle = "Neural Information Processing - 20th International Conference, ICONIP 2013, Proceedings",

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note = "20th International Conference on Neural Information Processing, ICONIP 2013 ; Conference date: 03-11-2013 Through 07-11-2013",

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Ma, B, Hu, H, Liu, S & Chen, J 2013, Robust visual tracking using local sparse covariance descriptor and matching pursuit. in Neural Information Processing - 20th International Conference, ICONIP 2013, Proceedings. PART 3 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 3, vol. 8228 LNCS, pp. 485-492, 20th International Conference on Neural Information Processing, ICONIP 2013, Daegu, Korea, Republic of, 3/11/13. https://doi.org/10.1007/978-3-642-42051-1_60

Robust visual tracking using local sparse covariance descriptor and matching pursuit. / Ma, Bo; Hu, Hongwei; Liu, Shiqi et al.
Neural Information Processing - 20th International Conference, ICONIP 2013, Proceedings. PART 3. ed. 2013. p. 485-492 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8228 LNCS, No. PART 3).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Robust visual tracking using local sparse covariance descriptor and matching pursuit

AU - Ma, Bo

AU - Hu, Hongwei

AU - Liu, Shiqi

AU - Chen, Jianglong

PY - 2013

Y1 - 2013

N2 - In this paper, we propose a visual tracking method based on local sparse covariance descriptor and matching pursuit. Covariance descriptor can model feature correlation of target templates effectively, and matching pursuit is employed to select the best target candidate which is reconstructed by target templates. The selection process is performed by solving a least square problem, and the candidate with the smallest projection error is taken as the tracking target. Experimental results on several video sequences demonstrate the good performance of proposed method compared with three existing tracking algorithms.

AB - In this paper, we propose a visual tracking method based on local sparse covariance descriptor and matching pursuit. Covariance descriptor can model feature correlation of target templates effectively, and matching pursuit is employed to select the best target candidate which is reconstructed by target templates. The selection process is performed by solving a least square problem, and the candidate with the smallest projection error is taken as the tracking target. Experimental results on several video sequences demonstrate the good performance of proposed method compared with three existing tracking algorithms.

KW - Covariance descriptor

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KW - Matching pursuit

KW - Visual tracking

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DO - 10.1007/978-3-642-42051-1_60

M3 - Conference contribution

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SN - 9783642420504

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - Neural Information Processing - 20th International Conference, ICONIP 2013, Proceedings

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Y2 - 3 November 2013 through 7 November 2013

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Ma B, Hu H, Liu S, Chen J. Robust visual tracking using local sparse covariance descriptor and matching pursuit. In Neural Information Processing - 20th International Conference, ICONIP 2013, Proceedings. PART 3 ed. 2013. p. 485-492. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 3). doi: 10.1007/978-3-642-42051-1_60

Robust visual tracking using local sparse covariance descriptor and matching pursuit

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