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Previous long-term tracking methods usually consist of multiple components, such as base trackers, target verification modules, and global detectors.
“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 [21]
Cites
Girshick, R. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.169 · Cited by 28,105 · see citing works
Object Detection with Discriminatively Trained Part-Based Models
Felzenszwalb, P. F., Girshick, R. B., McAllester, D., & Ramanan, D. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2009.167 · Cited by 9,853 · see citing works
Fully-Convolutional Siamese Networks for Object Tracking
Bertinetto, L., Valmadre, J., Henriques, J. F., Vedaldi, A., & Torr, P. H. S. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-48881-3_56 · Cited by 4,546 · see citing works
Wu, Y., Lim, J., & Yang, M.-H. (2015) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2014.2388226 · Cited by 3,371 · cited 8× · see citing works
“5 illustrates the precision and success plots on TempleColor dataset [30], which is containing 128 color videos while most of sequences are overlapped with OTB2015 dataset [28].”
MethodInfluentialECO: Efficient Convolution Operators for Tracking
Danelljan, M., Bhat, G., Khan, F. S., & Felsberg, M. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.733 · Cited by 2,805 · see citing works
Learning to Track at 100 FPS with Deep Regression Networks
Held, D., Thrun, S., & Savarese, S. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46448-0_45 · Cited by 1,199 · see citing works
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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 · see citing works
“Specifically, previous methods usually consist of multiple components, such as base trackers [9, 50], target verification modules [21], and global detectors [40, 19].”
MethodLightTrack: Finding Lightweight Neural Networks for Object Tracking via One-Shot Architecture Search
Yan, B., Peng, H., Wu, K., Wang, D., Fu, J., & Lu, H. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.01493 · Cited by 277 · see citing works
‘Skimming-Perusal’ Tracking: A Framework for Real-Time and Robust Long-Term Tracking
Yan, B., Zhao, H., Wang, D., Lu, H., & Yang, X. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00247 · Cited by 218 · see citing works
Tracking by Instance Detection: A Meta-Learning Approach
Wang, G., Luo, C., Sun, X., Xiong, Z., & Zeng, W. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00632 · Cited by 206 · see citing works
Siam R-CNN: Visual Tracking by Re-Detection
Voigtlaender, P., Luiten, J., Torr, P. H. S., & Leibe, B. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00661 · Cited by 115 · see citing works
Learning Target-Oriented Dual Attention for Robust RGB-T Tracking
Yang, R., Zhu, Y., Wang, X., Li, C., & Tang, J. (2019) · 2019 IEEE International Conference on Image Processing (ICIP) · doi:10.1109/icip.2019.8803528 · Cited by 45 · see citing works
- Authors
- Jung, I., Son, J., Baek, M., & Han, B.
- Affiliations
- Pohang University of Science and Technology (KR)
- Seoul National University (KR)
- Corresponding author
- Han, Bohyung
- ORCID
- Han, Bohyung
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2018
- Pages
- 89-104
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2888456413
- Cited by
- 297
- References
- 6
- Citations by year
- 2026: 11 · 2025: 19 · 2024: 33 · 2023: 47 · 2022: 50 · 2021: 56 · 2020: 50 · 2019: 29 · 2018: 2
- FWCI
- 10.32
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Lecture notes in computer science (publishedVersion)
- Seoul National University Open Repository (Seoul National University) (submittedVersion)
- 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, real-time tracking, loss function design, high-resolution feature maps, large receptive field
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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