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In this paper, we present LaSOT, a high-quality benchmark for Large-scaleSingle Object Tracking. LaSOT consists of 1,400 sequences with more than 3.5Mframes in total. Each frame in these sequences is carefully and manuallyannotated with a bounding box, making LaSOT the largest, to the best of ourknowledge, densely annotated tracking benchmark. The average video length ofLaSOT is more than 2,500 frames, and each sequence comprises various challengesderiving from the wild where target objects may disappear and re-appear againin the view. By releasing LaSOT, we expect to provide the community with alarge-scale dedicated benchmark with high quality for both the training of deeptrackers and the veritable evaluation of tracking algorithms. Moreover,considering the close connections of visual appearance and natural language, weenrich LaSOT by providing additional language specification, aiming atencouraging the exploration of natural linguistic feature for tracking. Athorough experimental evaluation of 35 tracking algorithms on LaSOT ispresented with detailed analysis, and the results demonstrate that there isstill a big room for improvements.

LaSOT: A High-quality Benchmark for Large-scale Single Object Tracking