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TrackingNet

A Large-Scale Dataset and Benchmark for Object Tracking in the Wild

TrackingNet

Despite the numerous developments in object tracking, further development of current tracking algorithms is limited by small and mostly saturated datasets. As a matter of fact, data-hungry trackers based on deep-learning currently rely on object detection datasets due to the scarcity of dedicated large-scale tracking datasets. In this work, we present TrackingNet, the first large-scale dataset and benchmark for object tracking in the wild. We provide more than 30K videos with more than 14 million dense bounding box annotations. Our dataset covers a wide selection of object classes in broad and diverse context. By releasing such a large-scale dataset, we expect deep trackers to further improve and generalize. In addition, we introduce a new benchmark composed of 500 novel videos, modeled with a distribution similar to our training dataset. By sequestering the annotation of the test set and providing an online evaluation server, we provide a fair benchmark for future development of object trackers.

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King Abdullah University of Science and Technology (KAUST)
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Task
Video Object Tracking
Annotation Types
Bounding Boxes
14000000
Items
Classes
Labels
Models using this dataset
Last updated on 
January 20, 2022
Licensed under 
Unknown
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