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.

中文翻译：

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PTB-TIR：热红外行人跟踪基准

热红外 (TIR) 行人跟踪是计算机视觉众多应用中的重要组成部分之一，它具有一个主要优势：它可以在完全黑暗的情况下跟踪行人。在基准数据集上公平评估 TIR 行人跟踪器的能力对于该领域的发展具有重要意义。但是，没有基准数据集。在本文中，我们为 TIR 行人跟踪器评估开发了一个 TIR 行人跟踪数据集。该数据集包括 60 个带有手动注释的热序列。每个序列有九个属性标签用于基于属性的评估。除了数据集之外，我们还使用九个公开可用的跟踪器对我们的基准数据集进行了大规模评估实验。实验结果有助于我们了解这些跟踪器的优缺点。此外，为了更深入地了解 TIR 行人跟踪器，我们将其功能分为三个部分：特征提取器、运动模型和观察模型。然后，我们对基准数据集进行了三个比较实验，以验证每个组件如何影响跟踪器的性能。这些实验的结果为未来的研究提供了一些指导。