CREST : Convolutional Residual Learning for Visual Tracking

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review

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Related Research Unit(s)

Detail(s)

Original languageEnglish
Title of host publicationIEEE International Conference on Computer Vision (ICCV) 2017
Subtitle of host publicationProceedings
PublisherIEEE
Pages2574-2583
ISBN (Electronic)9781538610329, 9781538610336
Publication statusPublished - Oct 2017

Publication series

Name
ISSN (Print)2380-7504

Conference

Title16th IEEE International Conference on Computer Vision, ICCV 2017
LocationVenice Convention Center
PlaceItaly
CityVenice
Period22 - 29 October 2017

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.

Bibliographic Note

Full text of this publication does not contain sufficient affiliation information. With consent from the author(s) concerned, the Research Unit(s) information for this record is based on the existing academic department affiliation of the author(s).

Citation Format(s)

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

IEEE International Conference on Computer Vision (ICCV) 2017: Proceedings. IEEE, 2017. p. 2574-2583 8237541.

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review