LSOTB-TIR:A Large-Scale High-Diversity Thermal Infrared Object Tracking Benchmark
This provides a standardized benchmark for researchers in computer vision to develop and evaluate deep learning-based TIR trackers, though it is incremental as it extends existing benchmarking efforts to the TIR domain.
The authors tackled the lack of large-scale benchmarks for thermal infrared (TIR) object tracking by creating LSOTB-TIR, a dataset with 1,400 sequences and over 600K frames, and showed that deep trackers achieve promising performance and that training on this dataset significantly improves their results.
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.