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

• URL: http://arxiv.org/abs/2504.14566v1
• Date: Sun, 20 Apr 2025 10:56:15 GMT
• Title: SMTT: Novel Structured Multi-task Tracking with Graph-Regularized Sparse Representation for Robust Thermal Infrared Target Tracking
• Authors: Shang Zhang, HuiPan Guan, XiaoBo Ding, Ruoyan Xiong, Yue Zhang,
• Abstract summary: Thermal infrared target tracking is crucial in applications such as surveillance, autonomous driving, and military operations.<n>In this paper, we propose a novel tracker, SMTT, which effectively addresses common challenges in thermal infrared imagery.
• Score: 8.52497147463548
• License: http://creativecommons.org/licenses/by/4.0/
• 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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