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LaSOT: A High-quality Large-scale Single Object Tracking Benchmark
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“LaSO-Text [23] is an extension of LaSOT, featuring a subset of 15 categories with 150 videos.”
From SAM2LONG: Enhancing SAM 2 for Long Video Segmentation with a Training-Free Memory Tree (2025) · cites this paper as [23]
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Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333
Microsoft COCO: Common Objects in Context
Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., & Zitnick, C. L. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10602-1_48 · Cited by 45,460
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380
The pascal visual object classes (VOC) challenge
Everingham, M., Van Gool, L., Williams, C. K. I., Winn, J., & Zisserman, A. (2010) · International Journal of Computer Vision · doi:10.1007/s11263-009-0275-4 · Cited by 19,599
The Cityscapes Dataset for Semantic Urban Scene Understanding
Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., & Schiele, B. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.350 · Cited by 12,433
High-Speed Tracking with Kernelized Correlation Filters
Henriques, J. F., Caseiro, R., Martins, P., & Batista, J. (2015) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2014.2345390 · Cited by 5,753
Unsupervised Domain Adaptation by BackpropagationPreprint
Ganin, Y., & Lempitsky, V. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1409.7495 · Cited by 5,441
Research works printed but not linked: 3
- Milan, A., Leal-Taixé, L., Reid, I., Roth, S., & Schindler, K. (2016). Mot16: A benchmark for multi-object tracking. arXiv preprint arXiv:1603.00831. · arXiv
- Valmadre, J., Bertinetto, L., Henriques, J., Vedaldi, A., Torr, P.H. (2017). End-to-end representation learning for correlation filter based tracking. In: CVPR. · DOI · Paper page
- Yosinski, J., Clune, J., Bengio, Y., & Lipson, H. (2014). How transferable are features in deep neural networks? In: NIPS.
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“To further evaluate the effectiveness of SAM2Long, we also apply it to three widely used single-object tracking benchmarks, namely LaSOT [22], LaSOText [23], and GOT-10k [30].”
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Visual object tracking: Review and challenges
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Robust Tracking via Combing Top-Down and Bottom-Up Attention
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SPTrack: Spectral Similarity Prompt Learning for Hyperspectral Object Tracking
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Enabling deformation slack in tracking with temporally even correlation filters
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Autoregressive Sequential Pretraining for Visual Tracking
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Progressive Semantic-Visual Alignment and Refinement for Vision-Language Tracking
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Mamba Adapter: Efficient Multi-Modal Fusion for Vision-Language Tracking
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Decoding split-frequency representation for cross-scale tracking
Zhang, Y., & Sun, H. (2025) · Neural Networks · doi:10.1016/j.neunet.2025.107587 · Cited by 11
SRRT: Exploring Search Region Regulation for Visual Object Tracking
Zhu, J., Chen, X. Y., Zhang, P., Wang, X., Wang, D., Zhao, W., & Lu, H. (2024) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2024.3409898 · Cited by 15
Robust Tracking via Fully Exploring Background Prior Knowledge
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SOTVerse: A User-Defined Task Space of Single Object Tracking
Hu, S., Zhao, X., & Huang, K. (2023) · International Journal of Computer Vision · doi:10.1007/s11263-023-01908-5 · Cited by 11
General cross-modality registration framework for visible and infrared UAV target image registration
Luo, Y., Cha, H., Zuo, L., Cheng, P., & Zhao, Q. (2023) · Scientific Reports · doi:10.1038/s41598-023-39863-3 · Cited by 10
LGTrack: Exploiting Local and Global Properties for Robust Visual Tracking
Liu, C., Zhao, J. W., Bo, C., Li, S., Wang, D., & Lu, H. (2024) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2024.3390054 · Cited by 10
CVT-Track: Concentrating on Valid Tokens for One-Stream Tracking
Li, J., Yuan, X., Qin, H., Wang, Y., Liu, X., & Xu, T. (2024) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2024.3452231 · Cited by 10
SiamET: a Siamese based visual tracking network with enhanced templates
Zhou, Y., & Zhang, Y. (2022) · Applied Intelligence · doi:10.1007/s10489-021-03057-z · Cited by 10
MIMTracking: Masked image modeling enhanced vision transformer for visual object tracking
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Exploring the Feature Extraction and Relation Modeling For Light-Weight Transformer Tracking
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Learning Distance Constrained Transformation for Video Tracking in Car-Following
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ScalableTrack: Scalable One-Stream Tracking via Alternating Learning
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Adaptive representation-aligned modeling for visual tracking
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DSTrack: Diffusion-based sequence learning for visual object tracking
Wang, Z., Wang, K., Tang, C., Li, X., Zhang, J., & Gao, L. (2025) · Pattern Recognition · doi:10.1016/j.patcog.2025.111694 · Cited by 8
Motion Deep Association for spatio-temporal object tracking
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Cross-alignment for efficient visual object tracking
Wang, S., Liang, M., Huang, S., Ning, J., & Cheng, G. (2025) · Pattern Recognition · doi:10.1016/j.patcog.2025.112048 · Cited by 8
Spatial-temporal single object tracking with three-way decision theory
Wang, Z., & Miao, D. (2022) · International Journal of Approximate Reasoning · doi:10.1016/j.ijar.2022.12.003 · Cited by 8
Adaptive sparse attention-based compact transformer for object tracking
Pan, F., Zhao, L., & Wang, C. (2024) · Scientific Reports · doi:10.1038/s41598-024-63028-5 · Cited by 7
Beyond traditional visual object tracking: a survey
Abdelaziz, O., Shehata, M., & Mohamed, M. (2024) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-024-02345-7 · Cited by 9
Visual Object Tracking With Multi-Frame Distractor Suppression
Han, Y., Cai, M., Wu, J., Bai, Z., Zhuo, T., Zhang, H., & Zhang, Y. (2024) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2024.3489797 · Cited by 8
A Transformer-based network with adaptive spatial prior for visual tracking
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SIEVL-Track: Exploring Semantic Information Enhancement for Visual-Language Object Tracking
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Learning Language Prompt for Vision-Language Tracking
Zong, C., Zhao, J. W., Chen, X. Y., Lu, H., & Wang, D. (2025) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2025.3557053 · Cited by 7
Gao, P., Xu, L., Tang, W.-J., Wang, F., Fujita, H., Aljuaid, H., & Yuan, R.-Y. (2025) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2025.3648551 · Cited by 7
Metadata
- Authors
- Fan, H., Bai, H., Lin, L., Yang, F., Chu, P., Deng, G., Yu, S., Harshit, Huang, M., Liu, J., Xu, Y., Liao, C., Yuan, L., & Ling, H.
- Affiliations
- Stony Brook University (US)
- Temple University (US)
- Peng Cheng Laboratory (CN)
- Amazon (United States) (US)
- Corresponding author
- Fan, Heng
- ORCID
- Venue
- International Journal of Computer Vision
- Published
- 2020-09-29
- Volume
- 129
- Issue
- 2
- Pages
- 439-461
- ISSN
- 0920-5691 (ISSN-L)
- 1573-1405
- OpenAlex
- W3090155371
- Cited by
- 360
- References
- 95
- Citations by year
- 2026: 91 · 2025: 116 · 2024: 77 · 2023: 43 · 2022: 24 · 2021: 8 · 2020: 1
- FWCI
- 7.39
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- International Journal of Computer Vision (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
- Keywords
- single object tracking, tracking benchmark, LaSOT, long-term tracking, target occlusion
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.
What counts as a claim ›How we measure confidence ›How we check this ›
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