Learning a temporally invariant representation for visual tracking

Chao Ma; Xiaokang Yang; Chongyang Zhang; Ming Hsuan Yang

doi:10.1109/ICIP.2015.7350921

Learning a temporally invariant representation for visual tracking

Chao Ma, Xiaokang Yang, Chongyang Zhang, Ming Hsuan Yang

College of Computing

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

12 Citations (Scopus)

Abstract

In this paper, we propose to learn temporally invariant features from a large number of image sequences to represent objects for visual tracking. These features are trained on a convolutional neural network with temporal invariance constraints and robust to diverse motion transformations. We employ linear correlation filters to encode the appearance templates of targets and perform the tracking task by searching for the maximum responses at each frame. The learned filters are updated online and adapt to significant appearance changes during tracking. Extensive experimental results on challenging sequences show that the proposed algorithm performs favorably against state-of-the-art methods in terms of efficiency, accuracy, and robustness.

Original language	English
Title of host publication	2015 IEEE International Conference on Image Processing, ICIP 2015 - Proceedings
Publisher	IEEE Computer Society
Pages	857-861
Number of pages	5
ISBN (Electronic)	9781479983391
DOIs	https://doi.org/10.1109/ICIP.2015.7350921
Publication status	Published - 2015 Dec 9
Event	IEEE International Conference on Image Processing, ICIP 2015 - Quebec City, Canada Duration: 2015 Sep 27 → 2015 Sep 30

Publication series

Name	Proceedings - International Conference on Image Processing, ICIP
Volume	2015-December
ISSN (Print)	1522-4880

Other

Other	IEEE International Conference on Image Processing, ICIP 2015
Country/Territory	Canada
City	Quebec City
Period	15/9/27 → 15/9/30

Bibliographical note

Publisher Copyright:
© 2015 IEEE.

All Science Journal Classification (ASJC) codes

Software
Computer Vision and Pattern Recognition
Signal Processing

Access to Document

10.1109/ICIP.2015.7350921

Cite this

Ma, C., Yang, X., Zhang, C., & Yang, M. H. (2015). Learning a temporally invariant representation for visual tracking. In 2015 IEEE International Conference on Image Processing, ICIP 2015 - Proceedings (pp. 857-861). [7350921] (Proceedings - International Conference on Image Processing, ICIP; Vol. 2015-December). IEEE Computer Society. https://doi.org/10.1109/ICIP.2015.7350921

@inproceedings{3a412c73fa484769a7dcc4b1ea58bc22,

title = "Learning a temporally invariant representation for visual tracking",

abstract = "In this paper, we propose to learn temporally invariant features from a large number of image sequences to represent objects for visual tracking. These features are trained on a convolutional neural network with temporal invariance constraints and robust to diverse motion transformations. We employ linear correlation filters to encode the appearance templates of targets and perform the tracking task by searching for the maximum responses at each frame. The learned filters are updated online and adapt to significant appearance changes during tracking. Extensive experimental results on challenging sequences show that the proposed algorithm performs favorably against state-of-the-art methods in terms of efficiency, accuracy, and robustness.",

author = "Chao Ma and Xiaokang Yang and Chongyang Zhang and Yang, {Ming Hsuan}",

note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; IEEE International Conference on Image Processing, ICIP 2015 ; Conference date: 27-09-2015 Through 30-09-2015",

year = "2015",

month = dec,

day = "9",

doi = "10.1109/ICIP.2015.7350921",

language = "English",

series = "Proceedings - International Conference on Image Processing, ICIP",

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Ma, C, Yang, X, Zhang, C & Yang, MH 2015, Learning a temporally invariant representation for visual tracking. in 2015 IEEE International Conference on Image Processing, ICIP 2015 - Proceedings., 7350921, Proceedings - International Conference on Image Processing, ICIP, vol. 2015-December, IEEE Computer Society, pp. 857-861, IEEE International Conference on Image Processing, ICIP 2015, Quebec City, Canada, 15/9/27. https://doi.org/10.1109/ICIP.2015.7350921

Learning a temporally invariant representation for visual tracking. / Ma, Chao; Yang, Xiaokang; Zhang, Chongyang et al.

2015 IEEE International Conference on Image Processing, ICIP 2015 - Proceedings. IEEE Computer Society, 2015. p. 857-861 7350921 (Proceedings - International Conference on Image Processing, ICIP; Vol. 2015-December).

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

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N2 - In this paper, we propose to learn temporally invariant features from a large number of image sequences to represent objects for visual tracking. These features are trained on a convolutional neural network with temporal invariance constraints and robust to diverse motion transformations. We employ linear correlation filters to encode the appearance templates of targets and perform the tracking task by searching for the maximum responses at each frame. The learned filters are updated online and adapt to significant appearance changes during tracking. Extensive experimental results on challenging sequences show that the proposed algorithm performs favorably against state-of-the-art methods in terms of efficiency, accuracy, and robustness.

AB - In this paper, we propose to learn temporally invariant features from a large number of image sequences to represent objects for visual tracking. These features are trained on a convolutional neural network with temporal invariance constraints and robust to diverse motion transformations. We employ linear correlation filters to encode the appearance templates of targets and perform the tracking task by searching for the maximum responses at each frame. The learned filters are updated online and adapt to significant appearance changes during tracking. Extensive experimental results on challenging sequences show that the proposed algorithm performs favorably against state-of-the-art methods in terms of efficiency, accuracy, and robustness.

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PB - IEEE Computer Society

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Learning a temporally invariant representation for visual tracking

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