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Cited by 514Video Surveillance and Tracking MethodsAdvanced Neural Network ApplicationsAutonomous Vehicle Technology and Safety

Unveiling the Power of Deep Tracking

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Bhat, Goutam, Johnander, Joakim, Danelljan, Martin, Khan, Fahad Shahbaz, Felsberg, Michael·2018·Lecture Notes in Computer Science
DOI 10.1007/978-3-030-01216-8_30
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  • STARKS50 achieves a competitive result on VOT2020, better than DiMP and UPDT.

    “Tab. 3 shows that STARKS50 achieves a competitive result, which is better than DiMP [3] and UPDT [4].”

    From Learning Spatio-Temporal Transformer for Visual Tracking (2021) · cites this paper as [4]

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Cited by

Sorted by citations

Authors
Bhat, G., Johnander, J., Danelljan, M., Khan, F. S., & Felsberg, M.
Affiliations
Linköping University (SE)
Corresponding author
Bhat, Goutam
ORCID
Venue
Lecture Notes in Computer Science
Publisher
Springer International Publishing
Published
2018
Pages
493-509
ISSN
  • 0302-9743 (ISSN-L)
  • 1611-3349
DOI
10.1007/978-3-030-01216-8_30
OpenAlex
W2797257757
Cited by
514
References
5
Citations by year
2026: 10 · 2025: 31 · 2024: 48 · 2023: 64 · 2022: 96 · 2021: 102 · 2020: 107 · 2019: 53 · 2018: 3
FWCI
19.97
Type
conference paper
Language
en
Open access
Licence
Locations
  • Lecture notes in computer science (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • DiVA (Linkoping University) (submittedVersion, other-oa)
  • arXiv (Cornell University)
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 › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
Keywords
deep features, shallow features, feature fusion, adaptive fusion, tracking robustness, tracking accuracy, low spatial resolution, limited training data

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