Integrating Boundary and Center Correlation Filters for Visual Tracking with Aspect Ratio Variation

Feng Li; Yingjie Yao; Peihua Li; David Zhang; Wangmeng Zuo; Ming Hsuan Yang

doi:10.1109/ICCVW.2017.234

Integrating Boundary and Center Correlation Filters for Visual Tracking with Aspect Ratio Variation

Feng Li, Yingjie Yao, Peihua Li, David Zhang, Wangmeng Zuo, Ming Hsuan Yang

College of Computing

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

112 Citations (Scopus)

Abstract

The aspect ratio variation frequently appears in visual tracking and has a severe influence on performance. Although many correlation filter (CF)-based trackers have also been suggested for scale adaptive tracking, few studies have been given to handle the aspect ratio variation for CF trackers. In this paper, we make the first attempt to address this issue by introducing a family of 1D boundary CFs to localize the left, right, top, and bottom boundaries in videos. This allows us cope with the aspect ratio variation flexibly during tracking. Specifically, we present a novel tracking model to integrate 1D Boundary and 2D Center CFs (IBCCF) where boundary and center filters are enforced by a near-orthogonality regularization term. To optimize our IBCCF model, we develop an alternating direction method of multipliers. Experiments on several datasets show that IBCCF can effectively handle aspect ratio variation, and achieves state-of-the-art performance in terms of accuracy and robustness.

Original language	English
Title of host publication	Proceedings - 2017 IEEE International Conference on Computer Vision Workshops, ICCVW 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2001-2009
Number of pages	9
ISBN (Electronic)	9781538610343
DOIs	https://doi.org/10.1109/ICCVW.2017.234
Publication status	Published - 2017 Jul 1
Event	16th IEEE International Conference on Computer Vision Workshops, ICCVW 2017 - Venice, Italy Duration: 2017 Oct 22 → 2017 Oct 29

Publication series

Name	Proceedings - 2017 IEEE International Conference on Computer Vision Workshops, ICCVW 2017
Volume	2018-January

Other

Other	16th IEEE International Conference on Computer Vision Workshops, ICCVW 2017
Country/Territory	Italy
City	Venice
Period	17/10/22 → 17/10/29

Bibliographical note

Publisher Copyright:
© 2017 IEEE.

All Science Journal Classification (ASJC) codes

Computer Science Applications
Computer Vision and Pattern Recognition

Access to Document

10.1109/ICCVW.2017.234

Cite this

Li, F., Yao, Y., Li, P., Zhang, D., Zuo, W., & Yang, M. H. (2017). Integrating Boundary and Center Correlation Filters for Visual Tracking with Aspect Ratio Variation. In Proceedings - 2017 IEEE International Conference on Computer Vision Workshops, ICCVW 2017 (pp. 2001-2009). (Proceedings - 2017 IEEE International Conference on Computer Vision Workshops, ICCVW 2017; Vol. 2018-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCVW.2017.234

Li, Feng ; Yao, Yingjie ; Li, Peihua et al. / Integrating Boundary and Center Correlation Filters for Visual Tracking with Aspect Ratio Variation. Proceedings - 2017 IEEE International Conference on Computer Vision Workshops, ICCVW 2017. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 2001-2009 (Proceedings - 2017 IEEE International Conference on Computer Vision Workshops, ICCVW 2017).

@inproceedings{651c178cb51541b39724fb99226b52b0,

title = "Integrating Boundary and Center Correlation Filters for Visual Tracking with Aspect Ratio Variation",

abstract = "The aspect ratio variation frequently appears in visual tracking and has a severe influence on performance. Although many correlation filter (CF)-based trackers have also been suggested for scale adaptive tracking, few studies have been given to handle the aspect ratio variation for CF trackers. In this paper, we make the first attempt to address this issue by introducing a family of 1D boundary CFs to localize the left, right, top, and bottom boundaries in videos. This allows us cope with the aspect ratio variation flexibly during tracking. Specifically, we present a novel tracking model to integrate 1D Boundary and 2D Center CFs (IBCCF) where boundary and center filters are enforced by a near-orthogonality regularization term. To optimize our IBCCF model, we develop an alternating direction method of multipliers. Experiments on several datasets show that IBCCF can effectively handle aspect ratio variation, and achieves state-of-the-art performance in terms of accuracy and robustness.",

author = "Feng Li and Yingjie Yao and Peihua Li and David Zhang and Wangmeng Zuo and Yang, {Ming Hsuan}",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 16th IEEE International Conference on Computer Vision Workshops, ICCVW 2017 ; Conference date: 22-10-2017 Through 29-10-2017",

year = "2017",

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doi = "10.1109/ICCVW.2017.234",

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series = "Proceedings - 2017 IEEE International Conference on Computer Vision Workshops, ICCVW 2017",

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Li, F, Yao, Y, Li, P, Zhang, D, Zuo, W & Yang, MH 2017, Integrating Boundary and Center Correlation Filters for Visual Tracking with Aspect Ratio Variation. in Proceedings - 2017 IEEE International Conference on Computer Vision Workshops, ICCVW 2017. Proceedings - 2017 IEEE International Conference on Computer Vision Workshops, ICCVW 2017, vol. 2018-January, Institute of Electrical and Electronics Engineers Inc., pp. 2001-2009, 16th IEEE International Conference on Computer Vision Workshops, ICCVW 2017, Venice, Italy, 17/10/22. https://doi.org/10.1109/ICCVW.2017.234

Integrating Boundary and Center Correlation Filters for Visual Tracking with Aspect Ratio Variation. / Li, Feng; Yao, Yingjie; Li, Peihua et al.
Proceedings - 2017 IEEE International Conference on Computer Vision Workshops, ICCVW 2017. Institute of Electrical and Electronics Engineers Inc., 2017. p. 2001-2009 (Proceedings - 2017 IEEE International Conference on Computer Vision Workshops, ICCVW 2017; Vol. 2018-January).

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

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AU - Li, Feng

AU - Yao, Yingjie

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AU - Yang, Ming Hsuan

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N2 - The aspect ratio variation frequently appears in visual tracking and has a severe influence on performance. Although many correlation filter (CF)-based trackers have also been suggested for scale adaptive tracking, few studies have been given to handle the aspect ratio variation for CF trackers. In this paper, we make the first attempt to address this issue by introducing a family of 1D boundary CFs to localize the left, right, top, and bottom boundaries in videos. This allows us cope with the aspect ratio variation flexibly during tracking. Specifically, we present a novel tracking model to integrate 1D Boundary and 2D Center CFs (IBCCF) where boundary and center filters are enforced by a near-orthogonality regularization term. To optimize our IBCCF model, we develop an alternating direction method of multipliers. Experiments on several datasets show that IBCCF can effectively handle aspect ratio variation, and achieves state-of-the-art performance in terms of accuracy and robustness.

AB - The aspect ratio variation frequently appears in visual tracking and has a severe influence on performance. Although many correlation filter (CF)-based trackers have also been suggested for scale adaptive tracking, few studies have been given to handle the aspect ratio variation for CF trackers. In this paper, we make the first attempt to address this issue by introducing a family of 1D boundary CFs to localize the left, right, top, and bottom boundaries in videos. This allows us cope with the aspect ratio variation flexibly during tracking. Specifically, we present a novel tracking model to integrate 1D Boundary and 2D Center CFs (IBCCF) where boundary and center filters are enforced by a near-orthogonality regularization term. To optimize our IBCCF model, we develop an alternating direction method of multipliers. Experiments on several datasets show that IBCCF can effectively handle aspect ratio variation, and achieves state-of-the-art performance in terms of accuracy and robustness.

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ER -

Li F, Yao Y, Li P, Zhang D, Zuo W, Yang MH. Integrating Boundary and Center Correlation Filters for Visual Tracking with Aspect Ratio Variation. In Proceedings - 2017 IEEE International Conference on Computer Vision Workshops, ICCVW 2017. Institute of Electrical and Electronics Engineers Inc. 2017. p. 2001-2009. (Proceedings - 2017 IEEE International Conference on Computer Vision Workshops, ICCVW 2017). doi: 10.1109/ICCVW.2017.234

Integrating Boundary and Center Correlation Filters for Visual Tracking with Aspect Ratio Variation

Abstract

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