基于图正则化稀疏表示的多任务追踪器，提升红外目标追踪鲁棒性。
SMTT: Novel Structured Multi-task Tracking with Graph-Regularized Sparse Representation for Robust Thermal Infrared Target Tracking
- 将追踪建模为多任务学习，分粒子优化并动态捕捉相似性。
- 在三个红外数据集上准确率领先，实现实时性能。
- 适合复杂场景下需要高鲁棒性的红外追踪应用。
热红外目标追踪在监控、自动驾驶和军事行动中至关重要。本文提出一种新型追踪器SMTT，通过多任务学习、联合稀疏表示和自适应图正则化，有效应对热红外图像中的噪声、遮挡和快速运动等挑战。将追踪任务重构为多任务学习问题，SMTT独立优化每个粒子的表征，并利用加权混合范数正则化策略动态捕捉空间与特征级相似性。为保证实时性，引入加速近端梯度法进行高效优化。在VOT-TIR、PTB-TIR和LSOTB-TIR等基准数据集上的大量实验表明，SMTT在精度、鲁棒性和计算效率方面均表现优异，证明其在复杂环境下的可靠性和高性能。
原文摘要 · Abstract (English)
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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