A large, high-diversity, one-shot database for generic object tracking in the wild
The dataset contains more than 10,000 video segments of real-world moving objects and over 1.5 million manually labeled bounding boxes.
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.
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.
The fair comparison of deep trackers is ensured with the protocol that all approaches are using the same training data provided by the dataset.
The dataset provides extra labels including object visible ratios and motion classes as additional supervision for handling specific challenges.
The test set embodies 84 object classes and 32 motion classes with only 180 video segments, allowing for efficient evaluation.
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