GOT-10k: Generic Object Tracking Benchmark

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.

Latest News

[2023.12.20] Recently, we have proposed a bionic drone-based single object tracking benchmark named BioDrone for robust vision research. Now you can download the dataset from the download page via the URL. The BioDrone paper has been accepted by International Journal of Computer Vision (IJCV)!

[2023.09.22] Recently, we have proposed a new multi-modal global instance tracking benchmark named MGIT. Now you can download the dataset from the download page via the URLs of VideoCube-Tiny. The MGIT paper has been accepted by the 37th Conference on Neural Information Processing Systems (NeurIPS 2023) Track on Datasets and Benchmark!

[2023.09.12] The SOTVerse paper has been accepted by International Journal of Computer Vision (IJCV)!

[2022.04.18] We have released SOTVerse, a user-defined task space of single object tracking, and the related paper has been released on arXiv.

[2022.03.29] The IEEE TPAMI paper is selected as "ESI Hot Papers"!

[2022.03.04] We have released VideoCube, a visual tracking intelligence evaluation platform for global instance tracking (GIT) task, and the GIT paper has been accepted by IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)!

[2021.09.14] The IEEE TPAMI paper is selected as "ESI Highly Cited Papers"!

[2019.11.16] GOT-10k paper has been accepted by IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)!

[2019.03.07] Submissions are allowed to be made public and visible on the leaderboard now!

[2019.01.23] A brief description of annotation files is provided at download page!

[2018.12.13] An up-to-date public leaderboard is now available!

[2018.12.11] A responsive evaluation server is ready for assessing submitted results!

[2018.12.11] The full dataset and annotations are available for downloading now!

Publications

GOT-10k: A Large High-Diversity Benchmark for Generic Object Tracking in the Wild.
L. Huang^*, X. Zhao^*, and K. Huang. ( ^*Equal contribution)
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI).
[PDF] [ArXiv] [BibTex]

Please cite this paper if GOT-10k helps your research.

Organizers

Lianghua Huang, Center for Research on Intelligent System and Engineering (CRISE), CASIA.

Xin Zhao, Center for Research on Intelligent System and Engineering (CRISE), CASIA.

Kaiqi Huang, Center for Research on Intelligent System and Engineering (CRISE), CASIA.