Progressive Domain Adaptation for Thermal Infrared Object Tracking

TL;DR

This paper introduces a Progressive Domain Adaptation framework that leverages large-scale RGB datasets and unlabeled TIR data to improve thermal infrared object tracking, addressing domain shift issues.

Contribution

It proposes a novel progressive domain adaptation approach combining adversarial global and clustering-based subdomain adaptation modules for TIR tracking.

Findings

Achieves nearly 6% improvement in success rate on TIR benchmarks.

Effectively reduces domain gap between RGB and TIR data.

Utilizes a large-scale unlabeled TIR dataset for training.

Abstract

Due to the lack of large-scale labeled Thermal InfraRed (TIR) training datasets, most existing TIR trackers are trained directly on RGB datasets. However, tracking methods trained on RGB datasets suffer a significant drop-off in TIR data due to the domain shift issue. To this end, in this work, we propose a Progressive Domain Adaptation framework for TIR Tracking (PDAT), which transfers useful knowledge learned from RGB tracking to TIR tracking. The framework makes full use of large-scale labeled RGB datasets without requiring time-consuming and labor-intensive labeling of large-scale TIR data. Specifically, we first propose an adversarial-based global domain adaptation module to reduce domain gap on the feature level coarsely. Second, we design a clustering-based subdomain adaptation method to further align the feature distributions of the RGB and TIR datasets finely. These two domain adaptation modules gradually eliminate the discrepancy between the two domains, and thus learn domain-invariant fine-grained features through progressive training. Additionally, we collect a largescale TIR dataset with over 1.48 million unlabeled TIR images for training the proposed domain adaptation framework. Experimental results on five TIR tracking benchmarks show that the proposed method gains a nearly 6% success rate, demonstrating its effectiveness.