University of Oulu

D. Li, Y. Kuai, G. Wen and L. Liu, "Robust Visual Tracking via Collaborative and Reinforced Convolutional Feature Learning," 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Long Beach, CA, USA, 2019, pp. 592-600, doi: 10.1109/CVPRW.2019.00085

Robust visual tracking via collaborative and reinforced convolutional feature learning

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Author: Li, Dongdong1; Kuai, Yangliu1; Wen, Gongjian1;
Organizations: 1College of Electronic Science and Technology, National University of Defense Technology, China
2College of System Engineering, National University of Defense Technology, China
3Center for Machine Vision and Signal Analysis, University of Oulu, Finland
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 0.3 MB)
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Language: English
Published: Institute of Electrical and Electronics Engineers, 2019
Publish Date: 2020-11-09


Convolutional neural networks are potent models that yield hierarchies of features and have drawn increasing interest in the visual tracking field. In the paper, we design an end-to-end trainable tracking framework based on Siamese network, which proposes to learn the low-level fine-grained and high-level semantic representations simultaneously with the aim of mutual benefit. Due to the distinct and complementary characteristics of the feature hierarchies, different tracking mechanisms are adopted for different feature layers. The low-level features are exploited and updated with a correlation filter layer for adaptive tracking and the high-level features are compared through cross-correlation directly for robust tracking. The two-level features are jointly trained with a multi-task loss function end-to-end. The proposed tracker takes full advantage of the adaptability of the low-level features and the generalization ability of the high-level features. Extensive experimental tracking results on the widely used OTB and TC128 benchmarks demonstrate the superiority of our tracker. Meanwhile, our proposed tracker can achieve a real-time tracking speed.

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Series: IEEE Computer Society Conference on Computer Vision and Pattern Recognition workshops
ISSN: 2160-7508
ISSN-E: 2160-7516
ISSN-L: 2160-7508
ISBN: 978-1-7281-2506-0
ISBN Print: 978-1-7281-2507-7
Pages: 592 - 600
Article number: 9025421
DOI: 10.1109/CVPRW.2019.00085
Host publication: 32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2019
Conference: IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops
Type of Publication: A4 Article in conference proceedings
Field of Science: 113 Computer and information sciences
213 Electronic, automation and communications engineering, electronics
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