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arXiv · 2504.14566

SMTT: Novel Structured Multi-task Tracking with Graph-Regularized Sparse Representation for Robust Thermal Infrared Target Tracking

Shang Zhang·HuiPan Guan·XiaoBo Ding·Ruoyan Xiong·Yue Zhang
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Abstract

Thermal infrared target tracking is crucial in applications such as surveillance, autonomous driving, and military operations. In this paper, we propose a novel tracker, SMTT, which effectively addresses common challenges in thermal infrared imagery, such as noise, occlusion, and rapid target motion, by leveraging multi-task learning, joint sparse representation, and adaptive graph regularization. By reformulating the tracking task as a multi-task learning problem, the SMTT tracker independently optimizes the representation of each particle while dynamically capturing spatial and feature-level similarities using a weighted mixed-norm regularization strategy. To ensure real-time performance, we incorporate the Accelerated Proximal Gradient method for efficient optimization. Extensive experiments on benchmark datasets - including VOT-TIR, PTB-TIR, and LSOTB-TIR - demonstrate that SMTT achieves superior accuracy, robustness, and computational efficiency. These results highlight SMTT as a reliable and high-performance solution for thermal infrared target tracking in complex environments.

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Shang Zhang, HuiPan Guan, XiaoBo Ding, Ruoyan Xiong, Yue Zhang. 2025-04-20. SMTT: Novel Structured Multi-task Tracking with Graph-Regularized Sparse Representation for Robust Thermal Infrared Target Tracking. https://arxiv.org/abs/2504.14566

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