TrackingNet: A Large-Scale Dataset and Benchmark for Object Tracking in the Wild
KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Electrical Engineering Program
Visual Computing Center (VCC)
Permanent link to this recordhttp://hdl.handle.net/10754/627423
MetadataShow full item record
AbstractDespite 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.
CitationMüller M, Bibi A, Giancola S, Alsubaihi S, Ghanem B (2018) TrackingNet: A Large-Scale Dataset and Benchmark for Object Tracking in the Wild. Lecture Notes in Computer Science: 310–327. Available: http://dx.doi.org/10.1007/978-3-030-01246-5_19.
SponsorsThis work was supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR). M. Müller, A. Bibi and S. Giancola—Equally contributed.
PublisherSpringer International Publishing
Conference/Event name15th European Conference on Computer Vision, ECCV 2018