PTB-TIR: A Thermal Infrared Pedestrian Tracking Benchmark
This provides a standardized evaluation tool for researchers in computer vision working on TIR pedestrian tracking, though it is incremental as it fills a gap rather than introducing new methods.
The authors tackled the lack of a benchmark dataset for thermal infrared (TIR) pedestrian tracking by creating PTB-TIR, which includes 60 sequences with manual annotations and attribute labels, and they evaluated nine trackers on it to analyze their performance.
Thermal infrared (TIR) pedestrian tracking is one of the important components among numerous applications of computer vision, which has a major advantage: it can track pedestrians in total darkness. The ability to evaluate the TIR pedestrian tracker fairly, on a benchmark dataset, is significant for the development of this field. However, there is not a benchmark dataset. In this paper, we develop a TIR pedestrian tracking dataset for the TIR pedestrian tracker evaluation. The dataset includes 60 thermal sequences with manual annotations. Each sequence has nine attribute labels for the attribute based evaluation. In addition to the dataset, we carry out the large-scale evaluation experiments on our benchmark dataset using nine publicly available trackers. The experimental results help us understand the strengths and weaknesses of these trackers.In addition, in order to gain more insight into the TIR pedestrian tracker, we divide its functions into three components: feature extractor, motion model, and observation model. Then, we conduct three comparison experiments on our benchmark dataset to validate how each component affects the tracker's performance. The findings of these experiments provide some guidelines for future research. The dataset and evaluation toolkit can be downloaded at {https://github.com/QiaoLiuHit/PTB-TIR_Evaluation_toolkit}.