GOT-10k: A Large High-Diversity Benchmark for Generic Object Tracking in the Wild
Open accessAbstract
We introduce here a large tracking database that offers an unprecedentedly wide coverage of common moving objects in the wild, called GOT-10k. Specifically, GOT-10k is built upon the backbone of WordNet structure [1] and it populates the majority of over 560 classes of moving objects and 87 motion patterns, magnitudes wider than the most recent similar-scale counterparts [19], [20], [23], [26]. By releasing the large high-diversity database, we aim to provide a unified training and evaluation platform for the development of class-agnostic, generic purposed short-term trackers. The features of GOT-10k and the contributions of this article are summarized in the following. (1) GOT-10k offers over 10,000 video segments with more than 1.5 million manually labeled bounding boxes, enabling unified training and stable evaluation of deep trackers. (2) GOT-10k is by far the first video trajectory dataset that uses the semantic hierarchy of WordNet to guide class population, which ensures a comprehensive and relatively unbiased coverage of diverse moving objects. (3) For the first time, GOT-10k introduces the one-shot protocol for tracker evaluation, where the training and test classes are zero-overlapped. The protocol avoids biased evaluation results towards familiar objects and it promotes generalization in tracker development. (4) GOT-10k offers additional labels such as motion classes and object visible ratios, facilitating the development of motion-aware and occlusion-aware trackers. (5) We conduct extensive tracking experiments with 39 typical tracking algorithms and their variants on GOT-10k and analyze their results in this paper. (6) Finally, we develop a comprehensive platform for the tracking community that offers full-featured evaluation toolkits, an online evaluation server, and a responsive leaderboard. The annotations of GOT-10k's test data are kept private to avoid tuning parameters on it.
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GOT-10K requires trackers to only use the training set of GOT-10K for model learning.
“GOT-10K requires trackers to only use the training set of GOT-10k for model learning.”
From Learning Spatio-Temporal Transformer for Visual Tracking (2021) · cites this paper as [18]
GOT-10k provides strict one-shot evaluation settings and diverse real-world tracking scenarios.
“It provides strict one-shot evaluation settings and diverse real-world tracking scenarios”
From SAM2LONG: Enhancing SAM 2 for Long Video Segmentation with a Training-Free Memory Tree (2025) · cites this paper as [30]
GOT-10k is a widely used benchmark for developing and assessing tracking models.
“making it a widely used benchmark for developing and assessing tracking models”
From SAM2LONG: Enhancing SAM 2 for Long Video Segmentation with a Training-Free Memory Tree (2025) · cites this paper as [30]
GOT-10k is a large-scale generic object tracking dataset that covers over 10,000 video sequences, spanning more than 560 object classes.
“GOT-10k [30] is a large-scale generic object tracking dataset that covers over 10,000 video sequences, spanning more than 560 object classes.”
From SAM2LONG: Enhancing SAM 2 for Long Video Segmentation with a Training-Free Memory Tree (2025) · cites this paper as [30]
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“This work presents a large unified tracking benchmark that solves the above problems by (1) Using WordNet [1] as the backbone to guide a comprehensive and unbiased population of object classes, and (2) Following the one-shot protocol to avoid biased evaluation results.”
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- Authors
- Huang, L., Zhao, X., & Huang, K.
- Affiliations
- Chinese Academy of Sciences (CN)
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2021-05-01
- Volume
- 43
- Issue
- 5
- Pages
- 1562-1577
- ISSN
- 0162-8828 (ISSN-L)
- 2160-9292
- 1939-3539
- OpenAlex
- W2898200825
- Cited by
- 1,809
- References
- 17
- Citations by year
- 2026: 209 · 2025: 323 · 2024: 355 · 2023: 345 · 2022: 253 · 2021: 198 · 2020: 101 · 2019: 25
- FWCI
- 64.47
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Transactions on Pattern Analysis and Machine Intelligence (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Key Research and Development Program of China (2016YFB1001001, 2016YFB1001005)
- National Natural Science Foundation of China (61602485, 61673375)
- Chinese Academy of Sciences (QYZDB-SSW-JSC006)
- 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
- tracking benchmark, movement patterns, object visibility
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