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 | |
| 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 | Canada |
| City | Quebec City |
| Period | 15/9/27 → 15/9/30 |
Fingerprint
All Science Journal Classification (ASJC) codes
- Software
- Computer Vision and Pattern Recognition
- Signal Processing
Cite this
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Learning a temporally invariant representation for visual tracking. / Ma, Chao; Yang, Xiaokang; Zhang, Chongyang; Yang, Ming Hsuan.
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
TY - GEN
T1 - Learning a temporally invariant representation for visual tracking
AU - Ma, Chao
AU - Yang, Xiaokang
AU - Zhang, Chongyang
AU - Yang, Ming Hsuan
PY - 2015/12/9
Y1 - 2015/12/9
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.
UR - http://www.scopus.com/inward/record.url?scp=84956694428&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84956694428&partnerID=8YFLogxK
U2 - 10.1109/ICIP.2015.7350921
DO - 10.1109/ICIP.2015.7350921
M3 - Conference contribution
AN - SCOPUS:84956694428
T3 - Proceedings - International Conference on Image Processing, ICIP
SP - 857
EP - 861
BT - 2015 IEEE International Conference on Image Processing, ICIP 2015 - Proceedings
PB - IEEE Computer Society
ER -