LSOTB-TIR：大规模高分集热红外目标跟踪基准
LSOTB-TIR:A Large-Scale High-Diversity Thermal Infrared Object Tracking Benchmark
摘要：
在本文中，我们提出了一个大规模和高多样性的通用热红外（TIR）目标跟踪基准，称为LSOTBTIR，它包括一个评估数据集和一个训练数据集，共有1,400个TIR序列和超过600 K帧。我们在所有序列的每一帧中标注对象的限界框，总共生成超过73万个边界框。据我们所知，LSOTB-TIR是迄今为止最大和最多样化的TIR对象跟踪基准。为了评估不同属性的跟踪器，我们在评估数据集中定义了4个场景属性和12个挑战属性。通过发布LSOTB-TIR，我们鼓励社区开发基于深度学习的TIR跟踪器，并对其进行公平和全面的评估。我们对LSOTB-TIR上的30多个跟踪器进行了评估和分析，以提供一系列基线，结果表明深度跟踪器具有良好的性能。此外，我们在LSOTB-TIR上重新训练了几个有代表性的深度跟踪器，结果表明，所提出的训练数据集显着提高了深度TIR跟踪器的性能。代码和数据集可在https://github.com/QiaoLiuHit/LSOTB-TIR上获得。
英文摘要：
In this paper, we present a Large-Scale and high-diversity general Thermal InfraRed (TIR) Object Tracking Benchmark, called LSOTBTIR, which consists of an evaluation dataset and a training dataset with a total of 1,400 TIR sequences and more than 600K frames. We annotate the bounding box of objects in every frame of all sequences and generate over 730K bounding boxes in total. To the best of our knowledge, LSOTB-TIR is the largest and most diverse TIR object tracking benchmark to date. To evaluate a tracker on different attributes, we define 4 scenario attributes and 12 challenge attributes in the evaluation dataset. By releasing LSOTB-TIR, we encourage the community to develop deep learning based TIR trackers and evaluate them fairly and comprehensively. We evaluate and analyze more than 30 trackers on LSOTB-TIR to provide a series of baselines, and the results show that deep trackers achieve promising performance. Furthermore, we re-train several representative deep trackers on LSOTB-TIR, and their results demonstrate that the proposed training dataset significantly improves the performance of deep TIR trackers. Codes and dataset are available at https://github.com/QiaoLiuHit/LSOTB-TIR.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
