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

计算机学院

Beijing Institute of Technology

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

2 引用（Scopus）

摘要

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.

源语言	英语
主期刊名	Neural Information Processing - 20th International Conference, ICONIP 2013, Proceedings
页	485-492
页数	8
版本	PART 3
DOI	https://doi.org/10.1007/978-3-642-42051-1_60
出版状态	已出版 - 2013
活动	20th International Conference on Neural Information Processing, ICONIP 2013 - Daegu, 韩国期限: 3 11月 2013 → 7 11月 2013

出版系列

姓名	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
编号	PART 3
卷	8228 LNCS
ISSN（印刷版）	0302-9743
ISSN（电子版）	1611-3349

会议

会议	20th International Conference on Neural Information Processing, ICONIP 2013
国家/地区	韩国
市	Daegu
时期	3/11/13 → 7/11/13

访问文件

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

其它文件与链接

链接到 Scopus 的出版物

引用此

Ma, B., Hu, H., Liu, S., & Chen, J. (2013). Robust visual tracking using local sparse covariance descriptor and matching pursuit. 在 Neural Information Processing - 20th International Conference, ICONIP 2013, Proceedings (PART 3 编辑, 页码 485-492). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); 卷 8228 LNCS, 号码 PART 3). https://doi.org/10.1007/978-3-642-42051-1_60

Ma, Bo ; Hu, Hongwei ; Liu, Shiqi 等. / Robust visual tracking using local sparse covariance descriptor and matching pursuit. Neural Information Processing - 20th International Conference, ICONIP 2013, Proceedings. PART 3. 编辑 2013. 页码 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",

edition = "PART 3",

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

}

Ma, B, Hu, H, Liu, S & Chen, J 2013, Robust visual tracking using local sparse covariance descriptor and matching pursuit. 在 Neural Information Processing - 20th International Conference, ICONIP 2013, Proceedings. PART 3 编辑, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 号码 PART 3, 卷 8228 LNCS, 页码 485-492, 20th International Conference on Neural Information Processing, ICONIP 2013, Daegu, 韩国, 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 等.
Neural Information Processing - 20th International Conference, ICONIP 2013, Proceedings. PART 3. 编辑 2013. 页码 485-492 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); 卷 8228 LNCS, 号码 PART 3).

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

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

KW - Local sparse descriptor

KW - Matching pursuit

KW - Visual tracking

UR - http://www.scopus.com/inward/record.url?scp=84893373803&partnerID=8YFLogxK

U2 - 10.1007/978-3-642-42051-1_60

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

M3 - Conference contribution

AN - SCOPUS:84893373803

SN - 9783642420504

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

SP - 485

EP - 492

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

T2 - 20th International Conference on Neural Information Processing, ICONIP 2013

Y2 - 3 November 2013 through 7 November 2013

ER -

Ma B, Hu H, Liu S, Chen J. Robust visual tracking using local sparse covariance descriptor and matching pursuit. 在 Neural Information Processing - 20th International Conference, ICONIP 2013, Proceedings. PART 3 编辑 2013. 页码 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

摘要

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会议

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引用此