The DOI matches its Crossref record; 42 of the 42 references it cites were found in a registry or an index.
GlobalTrack: A Simple and Strong Baseline for Long-Term Tracking
Open accessAbstract
A key capability of a long-term tracker is to search for targets in very large areas (typically the entire image) to handle possible target absences or tracking failures. However, currently there is a lack of such a strong baseline for global instance search. In this work, we aim to bridge this gap. Specifically, we propose GlobalTrack, a pure global instance search based tracker that makes no assumption on the temporal consistency of the target's positions and scales. GlobalTrack is developed based on two-stage object detectors, and it is able to perform full-image and multi-scale search of arbitrary instances with only a single query as the guide. We further propose a cross-query loss to improve the robustness of our approach against distractors. With no online learning, no punishment on position or scale changes, no scale smoothing and no trajectory refinement, our pure global instance search based tracker achieves comparable, sometimes much better performance on four large-scale tracking benchmarks (i.e., 52.1% AUC on LaSOT, 63.8% success rate on TLP, 60.3% MaxGM on OxUvA and 75.4% normalized precision on TrackingNet), compared to state-of-the-art approaches that typically require complex post-processing. More importantly, our tracker runs without cumulative errors, i.e., any type of temporary tracking failures will not affect its performance on future frames, making it ideal for long-term tracking. We hope this work will be a strong baseline for long-term tracking and will stimulate future works in this area.
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“Specifically, previous methods usually consist of multiple components, such as base trackers [9, 50], target verification modules [21], and global detectors [40, 19].”
From Learning Spatio-Temporal Transformer for Visual Tracking (2021) · cites this paper as [19]
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Cites
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MethodInfluentialATOM: Accurate Tracking by Overlap Maximization
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Self-citation
Flagged first, then by citations
Research works printed but not linked: 1
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Cited by
Sorted by citations
Learning Spatio-Temporal Transformer for Visual Tracking
Yan, B., Peng, H., Fu, J., Wang, D., & Lu, H. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01028 · Cited by 1,280
“To be specific, LTMU B takes the combination of ATOM [9] and SiamMask [50] as the short-term tracker, MDNet [37] as the verifier, and Global-Track [19] as the global detector.”
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Learning Target Candidate Association to Keep Track of What Not to Track
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STMTrack: Template-free Visual Tracking with Space-time Memory Networks
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Metadata
- Authors
- Huang, L., Zhao, X., & Huang, K.
- Affiliations
- Institute of Automation (CN)
- Venue
- Proceedings of the AAAI Conference on Artificial Intelligence
- Published
- 2020-04-03
- Volume
- 34
- Issue
- 07
- Pages
- 11037-11044
- ISSN
- 2159-5399 (ISSN-L)
- 2374-3468
- OpenAlex
- W2997896013
- Cited by
- 279
- References
- 36
- Citations by year
- 2026: 16 · 2025: 37 · 2024: 58 · 2023: 56 · 2022: 59 · 2021: 43 · 2020: 10
- FWCI
- 12.96
- Type
- conference paper
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- www.aaai.org/ (unspecified)
- Locations
- Proceedings of the AAAI Conference on Artificial Intelligence (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Natural Science Foundation of China (61602485, 61673375)
- National Key Research and Development Program of China (2016YFB1001005)
- 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
- long-term object tracking, distractor robustness
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