论文标题
LSOTB-TIR：大型高多样性热红外对象跟踪基准测试
LSOTB-TIR:A Large-Scale High-Diversity Thermal Infrared Object Tracking Benchmark
论文作者
论文摘要
在本文中，我们提出了一个大规模和高多样性的一般热红外（TIR）对象跟踪基准，称为LSOTBTIR，该基准由评估数据集和一个训练数据集组成，总计1,400 TIR序列和超过600K框架。我们在所有序列的每个帧中注释对象的边界框，并总共生成超过730k的边界框。据我们所知，LSOTB-TIR是迄今为止最大，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个方案属性和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，并公平，全面地评估它们。我们在LSOTB-TIR上评估和分析了30多个追踪器，以提供一系列基线，结果表明，深层跟踪器实现了有希望的性能。此外，我们重新培训了LSOTB-TIR的几个代表性跟踪器，其结果表明，所提出的训练数据集可显着提高深层TIR跟踪器的性能。代码和数据集可在https://github.com/qiaoliuhit/lsotb-tir上找到。
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.
