SMTT: Novel Structured Multi-task Tracking with Graph-Regularized Sparse Representation for Robust Thermal Infrared Target Tracking
This provides a reliable solution for surveillance, autonomous driving, and military operations, but it is incremental as it builds on existing multi-task and sparse representation methods.
The paper tackled thermal infrared target tracking by proposing the SMTT tracker, which uses multi-task learning and graph regularization to handle noise, occlusion, and rapid motion, achieving superior accuracy and computational efficiency on benchmark datasets like VOT-TIR, PTB-TIR, and LSOTB-TIR.
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.