Learning Unsupervised Cross-Domain Model for TIR Target Tracking

Xiu Shu, Feng Huang (), Zhaobing Qiu, Xinming Zhang and Di Yuan ()
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Xiu Shu: School of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou 510006, China
Feng Huang: School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou 350108, China
Zhaobing Qiu: School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou 350108, China
Xinming Zhang: School of Science, Harbin Institute of Technology, Shenzhen 518055, China
Di Yuan: Guangzhou Institute of Technology, Xidian University, Guangzhou 510555, China

Mathematics, 2024, vol. 12, issue 18, 1-15

Abstract: The limited availability of thermal infrared (TIR) training samples leads to suboptimal target representation by convolutional feature extraction networks, which adversely impacts the accuracy of TIR target tracking methods. To address this issue, we propose an unsupervised cross-domain model (UCDT) for TIR tracking. Our approach leverages labeled training samples from the RGB domain (source domain) to train a general feature extraction network. We then employ a cross-domain model to adapt this network for effective target feature extraction in the TIR domain (target domain). This cross-domain strategy addresses the challenge of limited TIR training samples effectively. Additionally, we utilize an unsupervised learning technique to generate pseudo-labels for unlabeled training samples in the source domain, which helps overcome the limitations imposed by the scarcity of annotated training data. Extensive experiments demonstrate that our UCDT tracking method outperforms existing tracking approaches on the PTB-TIR and LSOTB-TIR benchmarks.

Keywords: thermal infrared tracking; cross-domain model; unsupervised learning; feature extraction (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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