# SMMT: Siamese Motion Mamba with Self-attention for Thermal Infrared Target Tracking

arXiv: https://arxiv.org/abs/2505.04088 · HTML: https://mlsanity.com/paper/2505.04088/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Point clouds, detection and remote sensing, one of 10 directions in State-space sequence models (S4, Mamba).
- Builds on 24 of its 39 references that are in the map.
- First posted on arXiv 7 May 2025.

## Placed in

- State-space sequence models (S4, Mamba) / [Point clouds, detection and remote sensing](https://mlsanity.com/ssm/point-clouds-detection-and-remote-sensing/)

## Builds on (most cited first, 24 of 24)

- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023)
- [ByteTrack: Multi-Object Tracking by Associating Every Detection Box](https://mlsanity.com/paper/2110.06864/) (2021)
- [Fully-Convolutional Siamese Networks for Object Tracking](https://mlsanity.com/paper/1606.09549/) (2016)
- [SiamRPN++: Evolution of Siamese Visual Tracking with Very Deep Networks](https://mlsanity.com/paper/1812.11703/) (2018)
- [Observation-Centric SORT: Rethinking SORT for Robust Multi-Object Tracking](https://mlsanity.com/paper/2203.14360/) (2022)
- [ATOM: Accurate Tracking by Overlap Maximization](https://mlsanity.com/paper/1811.07628/) (2018)
- [ECO: Efficient Convolution Operators for Tracking](https://mlsanity.com/paper/1611.09224/) (2016)
- [MambaVision: A Hybrid Mamba-Transformer Vision Backbone](https://mlsanity.com/paper/2407.08083/) (2024)
- [Learning Multi-Domain Convolutional Neural Networks for Visual Tracking](https://mlsanity.com/paper/1510.07945/) (2015)
- [Chained-Tracker: Chaining Paired Attentive Regression Results for End-to-End Joint Multiple-Object Detection and Tracking](https://mlsanity.com/paper/2007.14557/) (2020)
- [Deformable Siamese Attention Networks for Visual Object Tracking](https://mlsanity.com/paper/2004.06711/) (2020)
- [VITAL: VIsual Tracking via Adversarial Learning](https://mlsanity.com/paper/1804.04273/) (2018)
- [Learning Spatial-Temporal Regularized Correlation Filters for Visual Tracking](https://mlsanity.com/paper/1803.08679/) (2018)
- [SiamMOT: Siamese Multi-Object Tracking](https://mlsanity.com/paper/2105.11595/) (2021)
- [Staple: Complementary Learners for Real-Time Tracking](https://mlsanity.com/paper/1512.01355/) (2015)
- [Target-Aware Deep Tracking](https://mlsanity.com/paper/1904.01772/) (2019)
- [SiamAPN++: Siamese Attentional Aggregation Network for Real-Time UAV Tracking](https://mlsanity.com/paper/2106.08816/) (2021)
- [PTB-TIR: A Thermal Infrared Pedestrian Tracking Benchmark](https://mlsanity.com/paper/1801.05944/) (2018)
- [Synthetic data generation for end-to-end thermal infrared tracking](https://mlsanity.com/paper/1806.01013/) (2018)
- [LSOTB-TIR:A Large-Scale High-Diversity Thermal Infrared Object Tracking Benchmark](https://mlsanity.com/paper/2008.00836/) (2020)
- [Learning Deep Multi-Level Similarity for Thermal Infrared Object Tracking](https://mlsanity.com/paper/1906.03568/) (2019)
- [FOLT: Fast Multiple Object Tracking from UAV-captured Videos Based on Optical Flow](https://mlsanity.com/paper/2308.07207/) (2023)
- [CEDNet: A Cascade Encoder-Decoder Network for Dense Prediction](https://mlsanity.com/paper/2302.06052/) (2023)
- [Learning Support Correlation Filters for Visual Tracking](https://mlsanity.com/paper/1601.06032/) (2016)

## Built on by (most cited first, 1 of 1)

- [E-TraMamba: A New Paradigm for Efficient Long-Term 3D Feature Tracking with Event Cameras](https://mlsanity.com/paper/2607.02866/) (2026)

## Abstract (arXiv)

Thermal infrared (TIR) object tracking often suffers from challenges such as target occlusion, motion blur, and background clutter, which significantly degrade the performance of trackers. To address these issues, this paper pro-poses a novel Siamese Motion Mamba Tracker (SMMT), which integrates a bidirectional state-space model and a self-attention mechanism. Specifically, we introduce the Motion Mamba module into the Siamese architecture to ex-tract motion features and recover overlooked edge details using bidirectional modeling and self-attention. We propose a Siamese parameter-sharing strate-gy that allows certain convolutional layers to share weights. This approach reduces computational redundancy while preserving strong feature represen-tation. In addition, we design a motion edge-aware regression loss to improve tracking accuracy, especially for motion-blurred targets. Extensive experi-ments are conducted on four TIR tracking benchmarks, including LSOTB-TIR, PTB-TIR, VOT-TIR2015, and VOT-TIR 2017. The results show that SMMT achieves superior performance in TIR target tracking.