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GOT-10k: A Large High-Diversity Benchmark for Generic Object Tracking in the Wild
文献类型：期刊论文
| 作者 | Huang, Lianghua3,4 ; Zhao, Xin3,4; Huang, Kaiqi1,2,3,4
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| 刊名 | IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
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| 出版日期 | 2021-05-01 |
| 卷号 | 43期号:5页码:1562-1577 |
| 关键词 | Training Object tracking Databases Protocols Benchmark testing Servers Object tracking benchmark dataset performance evaluation |
| ISSN号 | 0162-8828 |
| DOI | 10.1109/TPAMI.2019.2957464 |
| 通讯作者 | Huang, Lianghua(huanglianghua2017@ia.ac.cn) |
| 英文摘要 | 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. |
| 资助项目 | National Key Research and Development Program of China[2016YFB1001001] ; National Key Research and Development Program of China[2016YFB1001005] ; National Natural Science Foundation of China[61602485] ; National Natural Science Foundation of China[61673375] ; Projects of Chinese Academy of Science[QYZDB-SSW-JSC006] |
| WOS研究方向 | Computer Science ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:000637533800007 |
| 出版者 | IEEE COMPUTER SOC |
| 资助机构 | National Key Research and Development Program of China ; National Natural Science Foundation of China ; Projects of Chinese Academy of Science |
| 源URL | [http://ir.ia.ac.cn/handle/173211/44233]  |
| 专题 | 智能系统与工程 |
| 通讯作者 | Huang, Lianghua |
| 作者单位 | 1.CAS Ctr Excellence Brain Sci & Intelligence Techn, Beijing 100190, Peoples R China 2.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 4.Chinese Acad Sci, Ctr Res Intelligent Syst & Engn, Inst Automat, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Huang, Lianghua,Zhao, Xin,Huang, Kaiqi. GOT-10k: A Large High-Diversity Benchmark for Generic Object Tracking in the Wild[J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,2021,43(5):1562-1577. |
| APA | Huang, Lianghua,Zhao, Xin,&Huang, Kaiqi.(2021).GOT-10k: A Large High-Diversity Benchmark for Generic Object Tracking in the Wild.IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,43(5),1562-1577. |
| MLA | Huang, Lianghua,et al."GOT-10k: A Large High-Diversity Benchmark for Generic Object Tracking in the Wild".IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 43.5(2021):1562-1577. |
入库方式： OAI收割
来源：自动化研究所
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