PCA-Based appearance template learning for contour tracking

Bo Ma; Hongwei Hu; Pei Li; Yin Han

doi:10.1007/978-3-642-42051-1_61

PCA-Based appearance template learning for contour tracking

Bo Ma, Hongwei Hu, Pei Li, Yin Han

School of Computer Science and Technology

Beijing Institute of Technology

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

Abstract

A novel method is proposed in this paper to model changes of object appearance for object contour tracking. Principal component analysis is utilized to learn eigenvectors from a set of the object appearance in our work, and then the current object appearance can be reconstructed by a linear combination of the eigenvectors. To extract the object contour, we perform covariance matching under the variational level set framework. The proposed method is tested on several sequences under large variations, and demonstrates that it outperforms current methods without updating the appearance template.

Original language	English
Title of host publication	Neural Information Processing - 20th International Conference, ICONIP 2013, Proceedings
Pages	493-500
Number of pages	8
Edition	PART 3
DOIs	https://doi.org/10.1007/978-3-642-42051-1_61
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

Appearance template
Contour tracking
Covariance matrix
Level set
PCA

Access to Document

10.1007/978-3-642-42051-1_61

Cite this

Ma, B., Hu, H., Li, P., & Han, Y. (2013). PCA-Based appearance template learning for contour tracking. In Neural Information Processing - 20th International Conference, ICONIP 2013, Proceedings (PART 3 ed., pp. 493-500). (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_61

@inproceedings{b19f08b5f98241d7abcfc1b989a85941,

title = "PCA-Based appearance template learning for contour tracking",

abstract = "A novel method is proposed in this paper to model changes of object appearance for object contour tracking. Principal component analysis is utilized to learn eigenvectors from a set of the object appearance in our work, and then the current object appearance can be reconstructed by a linear combination of the eigenvectors. To extract the object contour, we perform covariance matching under the variational level set framework. The proposed method is tested on several sequences under large variations, and demonstrates that it outperforms current methods without updating the appearance template.",

keywords = "Appearance template, Contour tracking, Covariance matrix, Level set, PCA",

author = "Bo Ma and Hongwei Hu and Pei Li and Yin Han",

year = "2013",

doi = "10.1007/978-3-642-42051-1_61",

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 = "493--500",

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",

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Ma, B, Hu, H, Li, P & Han, Y 2013, PCA-Based appearance template learning for contour tracking. 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. 493-500, 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_61

PCA-Based appearance template learning for contour tracking. / Ma, Bo; Hu, Hongwei; Li, Pei et al.
Neural Information Processing - 20th International Conference, ICONIP 2013, Proceedings. PART 3. ed. 2013. p. 493-500 (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 - PCA-Based appearance template learning for contour tracking

AU - Ma, Bo

AU - Hu, Hongwei

AU - Li, Pei

AU - Han, Yin

PY - 2013

Y1 - 2013

N2 - A novel method is proposed in this paper to model changes of object appearance for object contour tracking. Principal component analysis is utilized to learn eigenvectors from a set of the object appearance in our work, and then the current object appearance can be reconstructed by a linear combination of the eigenvectors. To extract the object contour, we perform covariance matching under the variational level set framework. The proposed method is tested on several sequences under large variations, and demonstrates that it outperforms current methods without updating the appearance template.

AB - A novel method is proposed in this paper to model changes of object appearance for object contour tracking. Principal component analysis is utilized to learn eigenvectors from a set of the object appearance in our work, and then the current object appearance can be reconstructed by a linear combination of the eigenvectors. To extract the object contour, we perform covariance matching under the variational level set framework. The proposed method is tested on several sequences under large variations, and demonstrates that it outperforms current methods without updating the appearance template.

KW - Appearance template

KW - Contour tracking

KW - Covariance matrix

KW - Level set

KW - PCA

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U2 - 10.1007/978-3-642-42051-1_61

DO - 10.1007/978-3-642-42051-1_61

M3 - Conference contribution

AN - SCOPUS:84893372585

SN - 9783642420504

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

SP - 493

EP - 500

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, Li P, Han Y. PCA-Based appearance template learning for contour tracking. In Neural Information Processing - 20th International Conference, ICONIP 2013, Proceedings. PART 3 ed. 2013. p. 493-500. (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_61

PCA-Based appearance template learning for contour tracking

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