TrackingNet: A Large-Scale Dataset and
Benchmark for Object Tracking in the Wild ?
Abstract. Despite the numerous developments in object tracking, further improvement 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. Deep trackers fine-tuned on a fraction of our
dataset improve their performance by up to 1.6% on OTB100 and up
to 1.7% on TrackingNet Test. We provide an extensive benchmark on
TrackingNet by evaluating more than 20 trackers. Our results suggest
that object tracking in the wild is far from being solved.