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GOT-10k
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Jun 20, 2021 12:29 PM
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Overview

A large, high-diversity, one-shot database for generic object tracking in the wild

Key Features

  • Large-Scale
    The dataset contains more than 10,000 video segments of real-world moving objects and over 1.5 million manually labeled bounding boxes.
  • Generic Classes
    The dataset is backboned by WordNet and it covers a majority of 560+ classes of real-world moving objects and 80+ classes of motion patterns.
  • One-Shot
    The dataset encourages the development of generic purposed trackers by following the one-shot rule that object classes between train and test sets are zero-overlapped.
  • Unified Training Data
    The fair comparison of deep trackers is ensured with the protocol that all approaches are using the same training data provided by the dataset.
  • Extra Labeling
    The dataset provides extra labels including object visible ratios and motion classes as additional supervision for handling specific challenges.
  • Efficient Evaluation
    The test set embodies 84 object classes and 32 motion classes with only 180 video segments, allowing for efficient evaluation.

Paper

Please cite this paper if GOT-10k helps your research. [PDF] [BibTex]]

Data Annotation

Each sequence folder contains 4 annotation files and 1 meta file. A brief description of these files follows (let N denotes sequence length):

  • groundtruth.txt -- An N×4 matrix with each line representing object location [xmin, ymin, width, height] in one frame.
  • cover.label -- An N×1 array representing object visible ratios, with levels ranging from 0~8.
  • absense.label -- An binary N×1 array indicating whether an object is absent or present in each frame.
  • cut_by_image.label -- An binary N×1 array indicating whether an object is cut by image in each frame.
  • meta_info.ini -- Meta information about the sequence, including object and motion classes, video URL and more.

Values 0~8 in file cover.label correspond to ranges of object visible ratios: 0%, (0%, 15%], (15%~30%], (30%, 45%], (45%, 60%], (60%, 75%], (75%, 90%], (90%, 100%) and 100% respectively.

Data Format

The downloaded and extracted full dataset should follow the file structure:

|-- GOT-10k/
|-- train/
| |-- GOT-10k_Train_000001/
| | ......
| |-- GOT-10k_Train_009335/
| |-- list.txt
|-- val/
| |-- GOT-10k_Val_000001/
| | ......
| |-- GOT-10k_Val_000180/
| |-- list.txt
|-- test/
| |-- GOT-10k_Test_000001/
| | ......
| |-- GOT-10k_Test_000180/
| |-- list.txt

Instruction

Code

The benchmark offers light-weighted and compile-free toolkits written in pure Python and MATLAB. You will find tutorials and examples in the corresponding repositories.

Citation

@article{Huang_2019,
  title={GOT-10k: A Large High-Diversity Benchmark for Generic Object Tracking in the Wild},
  ISSN={1939-3539},
  url={http://dx.doi.org/10.1109/TPAMI.2019.2957464},
  DOI={10.1109/tpami.2019.2957464},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
  publisher={Institute of Electrical and Electronics Engineers (IEEE)},
  author={Huang, Lianghua and Zhao, Xin and Huang, Kaiqi},
  year={2019},
  pages={1–1}
 }
🎉Many thanks to Graviti Open Datasets for contributing the dataset
Basic Information
Application ScenariosNot Available
AnnotationsNot Available
TasksNot Available
LicenseCC BY-NC-SA 4.0
Updated on2021-01-20 04:08:00
Metadata
Data TypeNot Available
Data Volume0
Annotation Amount0
File Size0B
Copyright Owner
Institute of Automation Chinese Academy of Sciences
Annotator
Unknown
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