Progressive Unsupervised Learning for Visual Object Tracking

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

24 Scopus Citations
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Detail(s)

Original languageEnglish
Title of host publicationProceedings - 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition
Subtitle of host publicationCVPR 2021
PublisherInstitute of Electrical and Electronics Engineers, Inc.
Pages2992-3001
ISBN (electronic)9781665445092
ISBN (print)9781665445108
Publication statusPublished - 2021

Publication series

NameProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
ISSN (Print)1063-6919
ISSN (electronic)2575-7075

Conference

Title2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2021)
LocationVirtual
Period19 - 25 June 2021

Abstract

In this paper, we propose a progressive unsupervised learning (PUL) framework, which entirely removes the need for annotated training videos in visual tracking. Specifically, we first learn a background discrimination (BD) model that effectively distinguishes an object from background in a contrastive learning way. We then employ the BD model to progressively mine temporal corresponding patches (i.e., patches connected by a track) in sequential frames. As the BD model is imperfect and thus the mined patch pairs are noisy, we propose a noise-robust loss function to more effectively learn temporal correspondences from this noisy data. We use the proposed noise robust loss to train backbone networks of Siamese trackers. Without online fine-tuning or adaptation, our unsupervised real-time Siamese trackers can outperform state-of-the-art unsupervised deep trackers and achieve competitive results to the supervised baselines.

Citation Format(s)

Progressive Unsupervised Learning for Visual Object Tracking. / Wu, Qiangqiang; Wan, Jia; Chan, Antoni B.
Proceedings - 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition: CVPR 2021. Institute of Electrical and Electronics Engineers, Inc., 2021. p. 2992-3001 (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition).

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review