Abstract
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.
Keywords
Computer scienceArtificial intelligenceWordNetBenchmark (surveying)Eye trackingVideo trackingBitTorrent trackerMinimum bounding boxPopulationComputer visionInformation retrievalMachine learningObject (grammar)Image (mathematics)
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Publication Info
- Year
- 2019
- Type
- article
- Volume
- 43
- Issue
- 5
- Pages
- 1562-1577
- Citations
- 1605
- Access
- Closed
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Cite This
Lianghua Huang,
Xin Zhao,
Kaiqi Huang
(2019).
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.
https://doi.org/10.1109/tpami.2019.2957464
Identifiers
- DOI
- 10.1109/tpami.2019.2957464
- PMID
- 31804928
- arXiv
- 1810.11981