# A Survey for Deep RGBT Tracking

arXiv: https://arxiv.org/abs/2201.09296 · HTML: https://mlsanity.com/paper/2201.09296/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 51 references that are in the map.
- First posted on arXiv 23 Jan 2022.

## Builds on (most cited first, 19 of 19)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Fully-Convolutional Siamese Networks for Object Tracking](https://mlsanity.com/paper/1606.09549/) (2016)
- [GOT-10k: A Large High-Diversity Benchmark for Generic Object Tracking in the Wild](https://mlsanity.com/paper/1810.11981/) (2018)
- [SiamRPN++: Evolution of Siamese Visual Tracking with Very Deep Networks](https://mlsanity.com/paper/1812.11703/) (2018)
- [Learning Discriminative Model Prediction for Tracking](https://mlsanity.com/paper/1904.07220/) (2019)
- [Learning Multi-Domain Convolutional Neural Networks for Visual Tracking](https://mlsanity.com/paper/1510.07945/) (2015)
- [RGB-T Object Tracking:Benchmark and Baseline](https://mlsanity.com/paper/1805.08982/) (2018)
- [LasHeR: A Large-scale High-diversity Benchmark for RGBT Tracking](https://mlsanity.com/paper/2104.13202/) (2021)
- [Multi-Modal Fusion for End-to-End RGB-T Tracking](https://mlsanity.com/paper/1908.11714/) (2019)
- [NestFuse: An Infrared and Visible Image Fusion Architecture based on Nest Connection and Spatial/Channel Attention Models](https://mlsanity.com/paper/2007.00328/) (2020)
- [Challenge-Aware RGBT Tracking](https://mlsanity.com/paper/2007.13143/) (2020)
- [Jointly Modeling Motion and Appearance Cues for Robust RGB-T Tracking](https://mlsanity.com/paper/2007.02041/) (2020)
- [Dense Feature Aggregation and Pruning for RGBT Tracking](https://mlsanity.com/paper/1907.10451/) (2019)
- [Multi-Adapter RGBT Tracking](https://mlsanity.com/paper/1907.07485/) (2019)
- [RGBT Tracking via Multi-Adapter Network with Hierarchical Divergence Loss](https://mlsanity.com/paper/2011.07189/) (2020)
- [Learning Adaptive Discriminative Correlation Filters via Temporal Consistency Preserving Spatial Feature Selection for Robust Visual Tracking](https://mlsanity.com/paper/1807.11348/) (2018)
- [Multi-focus Image Fusion using dictionary learning and Low-Rank Representation](https://mlsanity.com/paper/1804.08355/) (2018)
- [Joint Group Feature Selection and Discriminative Filter Learning for Robust Visual Object Tracking](https://mlsanity.com/paper/1907.13242/) (2019)
- [Face Detection, Bounding Box Aggregation and Pose Estimation for Robust Facial Landmark Localisation in the Wild](https://mlsanity.com/paper/1705.02402/) (2017)

## Built on by (most cited first, 6 of 6)

- [RGB-T Tracking via Multi-Modal Mutual Prompt Learning](https://mlsanity.com/paper/2308.16386/) (2023)
- [RGB-T Tracking Based on Mixed Attention](https://mlsanity.com/paper/2304.04264/) (2023)
- [Awesome Multi-modal Object Tracking](https://mlsanity.com/paper/2405.14200/) (2024)
- [Omni Survey for Multimodality Analysis in Visual Object Tracking](https://mlsanity.com/paper/2508.13000/) (2025)
- [Middle Fusion and Multi-Stage, Multi-Form Prompts for Robust RGB-T Tracking](https://mlsanity.com/paper/2403.18193/) (2024)
- [EANet: Enhanced Attribute-based RGBT Tracker Network](https://mlsanity.com/paper/2307.01893/) (2023)

## Abstract (arXiv)

Visual object tracking with the visible (RGB) and thermal infrared (TIR) electromagnetic waves, shorted in RGBT tracking, recently draws increasing attention in the tracking community. Considering the rapid development of deep learning, a survey for the recent deep neural network based RGBT trackers is presented in this paper. Firstly, we give brief introduction for the RGBT trackers concluded into this category. Then, a comparison among the existing RGBT trackers on several challenging benchmarks is given statistically. Specifically, MDNet and Siamese architectures are the two mainstream frameworks in the RGBT community, especially the former. Trackers based on MDNet achieve higher performance while Siamese-based trackers satisfy the real-time requirement. In summary, since the large-scale dataset LasHeR is published, the integration of end-to-end framework, e.g., Siamese and Transformer, should be further considered to fulfil the real-time as well as more robust performance. Furthermore, the mathematical meaning should be more considered during designing the network. This survey can be treated as a look-up-table for researchers who are concerned about RGBT tracking.