PTB-TIR: A Thermal Infrared Pedestrian Tracking Benchmark

TL;DR

This paper introduces a new thermal infrared pedestrian tracking dataset with annotations and evaluates nine trackers, providing insights into their strengths and weaknesses, and analyzing the impact of different tracker components.

Contribution

The paper develops the first comprehensive TIR pedestrian tracking benchmark dataset and provides a large-scale evaluation of existing trackers with component analysis.

Findings

Nine trackers evaluated on the new dataset.

Component analysis reveals impact on tracking performance.

Guidelines for future TIR tracker development.

Abstract

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}.