CREST: Convolutional Residual Learning for Visual Tracking

Yibing Song, Chao Ma, Lijun Gong, Jiawei Zhang, Rynson W.H. Lau, Ming Hsuan Yang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

150 Citations (Scopus)

Abstract

Discriminative correlation filters (DCFs) have been shown to perform superiorly in visual tracking. They only need a small set of training samples from the initial frame to generate an appearance model. However, existing DCFs learn the filters separately from feature extraction, and update these filters using a moving average operation with an empirical weight. These DCF trackers hardly benefit from the end-to-end training. In this paper, we propose the CREST algorithm to reformulate DCFs as a one-layer convolutional neural network. Our method integrates feature extraction, response map generation as well as model update into the neural networks for an end-to-end training. To reduce model degradation during online update, we apply residual learning to take appearance changes into account. Extensive experiments on the benchmark datasets demonstrate that our CREST tracker performs favorably against state-of-the-art trackers.

Original languageEnglish
Title of host publicationProceedings - 2017 IEEE International Conference on Computer Vision, ICCV 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2574-2583
Number of pages10
ISBN (Electronic)9781538610329
DOIs
Publication statusPublished - 2017 Dec 22
Event16th IEEE International Conference on Computer Vision, ICCV 2017 - Venice, Italy
Duration: 2017 Oct 222017 Oct 29

Publication series

NameProceedings of the IEEE International Conference on Computer Vision
Volume2017-October
ISSN (Print)1550-5499

Other

Other16th IEEE International Conference on Computer Vision, ICCV 2017
CountryItaly
CityVenice
Period17/10/2217/10/29

Fingerprint

Feature extraction
Neural networks
Degradation
Experiments

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition

Cite this

Song, Y., Ma, C., Gong, L., Zhang, J., Lau, R. W. H., & Yang, M. H. (2017). CREST: Convolutional Residual Learning for Visual Tracking. In Proceedings - 2017 IEEE International Conference on Computer Vision, ICCV 2017 (pp. 2574-2583). [8237541] (Proceedings of the IEEE International Conference on Computer Vision; Vol. 2017-October). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCV.2017.279
Song, Yibing ; Ma, Chao ; Gong, Lijun ; Zhang, Jiawei ; Lau, Rynson W.H. ; Yang, Ming Hsuan. / CREST : Convolutional Residual Learning for Visual Tracking. Proceedings - 2017 IEEE International Conference on Computer Vision, ICCV 2017. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 2574-2583 (Proceedings of the IEEE International Conference on Computer Vision).
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Song, Y, Ma, C, Gong, L, Zhang, J, Lau, RWH & Yang, MH 2017, CREST: Convolutional Residual Learning for Visual Tracking. in Proceedings - 2017 IEEE International Conference on Computer Vision, ICCV 2017., 8237541, Proceedings of the IEEE International Conference on Computer Vision, vol. 2017-October, Institute of Electrical and Electronics Engineers Inc., pp. 2574-2583, 16th IEEE International Conference on Computer Vision, ICCV 2017, Venice, Italy, 17/10/22. https://doi.org/10.1109/ICCV.2017.279

CREST : Convolutional Residual Learning for Visual Tracking. / Song, Yibing; Ma, Chao; Gong, Lijun; Zhang, Jiawei; Lau, Rynson W.H.; Yang, Ming Hsuan.

Proceedings - 2017 IEEE International Conference on Computer Vision, ICCV 2017. Institute of Electrical and Electronics Engineers Inc., 2017. p. 2574-2583 8237541 (Proceedings of the IEEE International Conference on Computer Vision; Vol. 2017-October).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Song Y, Ma C, Gong L, Zhang J, Lau RWH, Yang MH. CREST: Convolutional Residual Learning for Visual Tracking. In Proceedings - 2017 IEEE International Conference on Computer Vision, ICCV 2017. Institute of Electrical and Electronics Engineers Inc. 2017. p. 2574-2583. 8237541. (Proceedings of the IEEE International Conference on Computer Vision). https://doi.org/10.1109/ICCV.2017.279