Deep visual tracking: Review and experimental comparison
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Previous models have to improve nonlinear representation ability by introducing fashion structures, using additional modules, or designing effective online updaters.
“Thus, previous models have to improve the nonlinear representation ability by introducing fashion structures [21, 44, 48], using additional modules [7, 46, 13], designing effective online updaters [2, 47, 10], to name a few.”
From Transformer Tracking (2021) · cites this paper as [21]
Prior work such as SiamRPN, SiamRPN++, and Ocean selects final tracking results using post-processing schemes including cosine window penalty, scale change penalty, and bounding box smoothing.
“In the prior work such as SiamRPN [22], SiamRPN++ [21] and Ocean [48], the final tracking results are selected by post-processing schemes including cosine window penalty, scale change penalty and bounding box smoothing.”
From Transformer Tracking (2021) · cites this paper as [21]
On OTB2015, TransT achieves comparable results with state-of-the-art algorithms such as PrDiMP and SiamRPN++.
“Table 4 shows that our method achieves comparable results with state-of-the-art algorithms (such as PrDiMP and SiamRPN++).”
From Transformer Tracking (2021) · cites this paper as [21]
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LaSOT: A High-Quality Benchmark for Large-Scale Single Object Tracking
Fan, H., Lin, L., Yang, F., Chu, P., Deng, G., Yu, S., Bai, H., Xu, Y., Liao, C., & Ling, H. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00552 · Cited by 1,742 · see citing works
“In this section, we mainly focus on the tracking benchmarks that are relevant to our work, and refer the readers to surveys [32, 47, 54, 30] for tracking algorithms.”
BackgroundChen, X., Yan, B., Zhu, J., Wang, D., Yang, X., & Lu, H. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00803 · Cited by 1,588 · see citing works
“Many efforts have been done in recent years [23, 28], however, designing a high-accuracy and real-time tracker is still a challenging task.”
BackgroundSelf-citationLearning 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
Self-citationLaSOT: A High-quality Large-scale Single Object Tracking Benchmark
Fan, H., Bai, H., Lin, L., Yang, F., Chu, P., Deng, G., Yu, S., Harshit, Huang, M., Liu, J., et al. (2021) · International Journal of Computer Vision · doi:10.1007/s11263-020-01387-y · Cited by 360 · see citing works
GradNet: Gradient-Guided Network for Visual Object Tracking
Li, P., Chen, B., Ouyang, W., Wang, D., Yang, X., & Lu, H. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00626 · Cited by 348 · see citing works
Self-citation‘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
Self-citationLong short-term memory-based deep recurrent neural networks for target tracking
Gao, C., Yan, J., Zhou, S., Varshney, P. K., & Liu, H. (2019) · Information Sciences · doi:10.1016/j.ins.2019.06.039 · Cited by 118 · see citing works
A survey on online learning for visual tracking
Abbass, M. Y., Kwon, K.-C., Kim, N., Abdelwahab, S. A., El-Samie, F. E. A., & Khalaf, A. A. M. (2021) · The Visual Computer · doi:10.1007/s00371-020-01848-y · Cited by 64 · see citing works
Abbas, W., & Masip Rodo, D. (2019) · Sensors · doi:10.3390/s19153274 · Cited by 21 · see citing works
- Authors
- Li, P., Wang, D., Wang, L., & Lu, H.
- Affiliations
- Dalian University of Technology (CN)
- Corresponding author
- Lu, Huchuan
- Venue
- Pattern Recognition
- Publisher
- Elsevier BV
- Published
- 2018-04
- Volume
- 76
- Pages
- 323-338
- ISSN
- 0031-3203 (ISSN-L)
- OpenAlex
- W2767302379
- Cited by
- 517
- References
- 111
- Citations by year
- 2026: 7 · 2025: 18 · 2024: 39 · 2023: 51 · 2022: 84 · 2021: 90 · 2020: 94 · 2019: 94 · 2018: 36 · 2017: 1 · 2016: 3
- FWCI
- 25.55
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Pattern Recognition (publishedVersion)
- 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
- object tracking, convolutional neural networks, deep features, template matching, end-to-end learning, online fine-tuning, benchmark comparison
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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.
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