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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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“To achieve this, we utilized sequences containing cars from the LaSOT [23] dataset as the test sequence for our OAL.”
BackgroundMethodHistorical states modeling for visual tracking
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PlanarTrack: A high-quality and challenging benchmark for large-scale planar object tracking
Jiao, Y., Liu, X., Liu, X., Yuan, X., Fan, H., & Zhang, L. (2025) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2025.104553 · Cited by 1
Self-citationHigh-Performance Discriminative Tracking with Spatio-Temporal Template Fusion
He, X., Xu, H., Zhu, X., & Li, H. (2025) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3746027.3755721 · Cited by 1
Target Tracking with Adaptive Morphological Correlation and Neural Predictive Modeling
Diaz-Ramirez, V. H., & Gaxiola, L. N. (2025) · Applied Sciences · doi:10.3390/app152111406 · Cited by 1
TIFTrack: Visual Object Tracking by Target Trajectory and Inter Frame Relationship
Bi, Z., Lu, H., Zhang, W., & Dai, D. (2025) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn64981.2025.11227629 · Cited by 1
Lightweight Multimodal Adapter for Visual Object Tracking
Borsuk, V., Yakovyna, V. S., & Shakhovska, N. (2025) · Big Data and Cognitive Computing · doi:10.3390/bdcc9110292 · Cited by 1
Efficient and Accurate Low-Resolution Transformer Tracking
Dong, S., Feng, Y., Liang, J., Yang, Q., Lin, Y., & Fan, H. (2025) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros60139.2025.11247730 · Cited by 1
Self-citation
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.
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