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

arXiv: https://arxiv.org/abs/2504.14566 · HTML: https://mlsanity.com/paper/2504.14566/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 30 references that are in the map.
- First posted on arXiv 20 Apr 2025.

## Builds on (most cited first, 6 of 6)

- [Fully-Convolutional Siamese Networks for Object Tracking](https://mlsanity.com/paper/1606.09549/) (2016)
- [PTB-TIR: A Thermal Infrared Pedestrian Tracking Benchmark](https://mlsanity.com/paper/1801.05944/) (2018)
- [LSOTB-TIR:A Large-Scale High-Diversity Thermal Infrared Object Tracking Benchmark](https://mlsanity.com/paper/2008.00836/) (2020)
- [Hierarchical Spatial-aware Siamese Network for Thermal Infrared Object Tracking](https://mlsanity.com/paper/1711.09539/) (2017)
- [Large Margin Structured Convolution Operator for Thermal Infrared Object Tracking](https://mlsanity.com/paper/1804.07006/) (2018)
- [Learning Rates for Multi-task Regularization Networks](https://mlsanity.com/paper/2104.00453/) (2021)

## Abstract (arXiv)

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.