GOT-10k: Generic Object Tracking Benchmark

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.

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Publication

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] [BibTex]

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

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