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Tracking by natural language specification is a new rising research topicthat aims at locating the target object in the video sequence based on itslanguage description. Compared with traditional bounding box (BBox) basedtracking, this setting guides object tracking with high-level semanticinformation, addresses the ambiguity of BBox, and links local and global searchorganically together. Those benefits may bring more flexible, robust andaccurate tracking performance in practical scenarios. However, existing naturallanguage initialized trackers are developed and compared on benchmark datasetsproposed for tracking-by-BBox, which can't reflect the true power oftracking-by-language. In this work, we propose a new benchmark specificallydedicated to the tracking-by-language, including a large scale dataset, strongand diverse baseline methods. Specifically, we collect 2k video sequences(contains a total of 1,244,340 frames, 663 words) and split 1300/700 for thetrain/testing respectively. We densely annotate one sentence in English andcorresponding bounding boxes of the target object for each video. We alsointroduce two new challenges into TNL2K for the object tracking task, i.e.,adversarial samples and modality switch. A strong baseline method based on anadaptive local-global-search scheme is proposed for future works to compare. Webelieve this benchmark will greatly boost related researches on naturallanguage guided tracking.

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