Unsupervised Domain Adaptive Thermal Infrared Tracking (IEEE TMM 2025)

Abstract

We propose an unsupervised Dual-level Domain Adaptation TIR Tracking framework (DDAT), which can benefit from training on large-scale labeled RGB datasets and unlabeled TIR datasets. Specifically, to transfer the useful knowledge learned from the RGB dataset to TIR tracking, we first propose an adversarial-based adaptation module on both the semantic-level and the feature-level. While the semantic-level adaptation can reduce the semantic gap between the TIR and RGB tracking tasks, the feature-level adaptation can learn domain-invariant features for more robust tracking. Second, we propose a partial domain adaptation module to alleviate the negative transfer problem because the RGB and TIR tracking domains have non-identical class and feature spaces. Instead of aligning the entire feature space, this module adaptively selects partial similarity samples and features for alignment, thus obtaining more fine-grained aligned results. Third, we collect the currently largest-scale unlabeled TIR dataset to train the proposed framework.

Download

• You can download our trained models from Baidu Pan. Extraction Code: 1111

• We provide a raw result of DDAT on the LSOTB-TIR100, LSOTB-TIR120, and PTB-TIR benchmarks in here. Extraction Code: 1111

Usage

Tracking

• Clone the code and unzip it on your computer.
• Prerequisites: Ubuntu 22.04, Pytorch 2.2.2, GTX A100, CUDA 12.1.
• Download our trained models from here.
• Run pysot_toolkit/test.py to test a TIR sequence using the default model.

Citation

If you use the code or dataset, please consider citing our paper.

Contact

Feedback and comments are welcome!
Feel free to contact us via liuqiao.hit@gmail.com or liuqiao@stu.hit.edu.cn.