GradNet: Gradient-Guided Network for Visual Object Tracking
Open accessAbstract
The fully-convolutional siamese network based on template matching has shown great potentials in visual tracking. During testing, the template is fixed with the initial target feature and the performance totally relies on the general matching ability of the siamese network. However, this manner cannot capture the temporal variations of targets or background clutter. In this work, we propose a novel gradient-guided network to exploit the discriminative information in gradients and update the template in the siamese network through feed-forward and backward operations. To be specific, the algorithm can utilize the information from the gradient to update the template in the current frame. In addition, a template generalization training method is proposed to better use gradient information and avoid overfitting. To our knowledge, this work is the first attempt to exploit the information in the gradient for template update in siamese-based trackers. Extensive experiments on recent benchmarks demonstrate that our method achieves better performance than other state-of-the-art trackers.
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- Authors
- Li, P., Chen, B., Ouyang, W., Wang, D., Yang, X., & Lu, H.
- Affiliations
- Dalian University of Technology (CN)
- The University of Sydney (AU)
- Venue
- 2019 IEEE/CVF International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2019-10
- Pages
- 6161-6170
- OpenAlex
- W2989688045
- Cited by
- 348
- References
- 47
- Citations by year
- 2026: 5 · 2025: 22 · 2024: 43 · 2023: 69 · 2022: 73 · 2021: 84 · 2020: 51 · 2019: 1
- FWCI
- 17.03
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE/CVF International Conference on Computer Vision (ICCV) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Natural Science Foundation of China (61725202, 61829102, 61751212)
- Fundamental Research Funds for the Central Universities (DUT19GJ201, DUT18JC30)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
- Keywords
- visual object tracking, Siamese network, template updating, template matching, background clutter, overfitting avoidance
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