TNL2K (Tracking by natural language)

Introduced by Wang et al. in Towards More Flexible and Accurate Object Tracking with Natural Language: Algorithms and Benchmark

Tracking by Natural Language (TNL2K) is constructed for the evaluation of tracking by natural language specification. TNL2K features:

Large-scale: 2,000 sequences, contains 1,244,340 frames, 663 words, 1300 / 700 for the train / testing respectively
High-quality: Manual annotation with careful inspection in each frame
Multi-modal: Providing visual and language annotation for each sequence
Adversarial-samples: Randomly adding adversarial samples for research on adversarial attack and defence
Significant-appearance-variation: Containing videos with cloth/face change for pedestrian
Heterogeneous: Containing RGB, thermal, Cartoon, Synthetic data
Multiple-baseline: Tracking-by-BBox, Tracking-by-Language, Tracking-by-Joint-BBox-Language

Source: Towards More Flexible and Accurate Object Tracking with Natural Language: Algorithms and Benchmark

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Trend	Task	Dataset Variant	Best Model	Paper	Code
	Visual Object Tracking	TNL2K	ODTrack-L
	Visual Tracking	TNL2K	ARTrack-L

Paper	Code	Results	Date	Stars

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