PTB-TIR: A Thermal Infrared Pedestrian Tracking Benchmark

Thermal infrared (TIR) pedestrian tracking is one of the most important components in numerous applications of computer vision, which has a major advantage: it can track the pedestrians in total darkness. How to evaluate the TIR pedestrian tracker fairly on a benchmark dataset is significant for the development of this field. However, there is no 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 public available trackers. The experimental results help us to understand the strength and weakness of these trackers. What’s more, in order to get insight into the TIR pedestrian tracker more sufficiently, we divide a tracker 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.