# Revisiting RGBT Tracking Benchmarks from the Perspective of Modality Validity: A New Benchmark, Problem, and Solution

arXiv: https://arxiv.org/abs/2405.00168 · HTML: https://mlsanity.com/paper/2405.00168/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 23 of its 46 references that are in the map.
- First posted on arXiv 30 Apr 2024.

## Builds on (most cited first, 23 of 23)

- [LoFTR: Detector-Free Local Feature Matching with Transformers](https://mlsanity.com/paper/2104.00680/) (2021)
- [TrackingNet: A Large-Scale Dataset and Benchmark for Object Tracking in the Wild](https://mlsanity.com/paper/1803.10794/) (2018)
- [Joint Feature Learning and Relation Modeling for Tracking: A One-Stream Framework](https://mlsanity.com/paper/2203.11991/) (2022)
- [Visual Prompt Multi-Modal Tracking](https://mlsanity.com/paper/2303.10826/) (2023)
- [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)
- [Visible-Thermal UAV Tracking: A Large-Scale Benchmark and New Baseline](https://mlsanity.com/paper/2204.04120/) (2022)
- [Multi-Modal Fusion for End-to-End RGB-T Tracking](https://mlsanity.com/paper/1908.11714/) (2019)
- [Prompting for Multi-Modal Tracking](https://mlsanity.com/paper/2207.14571/) (2022)
- [Challenge-Aware RGBT Tracking](https://mlsanity.com/paper/2007.13143/) (2020)
- [Revisiting Color-Event based Tracking: A Unified Network, Dataset, and Metric](https://mlsanity.com/paper/2211.11010/) (2022)
- [Jointly Modeling Motion and Appearance Cues for Robust RGB-T Tracking](https://mlsanity.com/paper/2007.02041/) (2020)
- [Bi-directional Adapter for Multi-modal Tracking](https://mlsanity.com/paper/2312.10611/) (2023)
- [RGBD1K: A Large-scale Dataset and Benchmark for RGB-D Object Tracking](https://mlsanity.com/paper/2208.09787/) (2022)
- [Multi-Adapter RGBT Tracking](https://mlsanity.com/paper/1907.07485/) (2019)
- [Duality-Gated Mutual Condition Network for RGBT Tracking](https://mlsanity.com/paper/2011.07188/) (2020)
- [Generative-based Fusion Mechanism for Multi-Modal Tracking](https://mlsanity.com/paper/2309.01728/) (2023)
- [RGBT Tracking via Multi-Adapter Network with Hierarchical Divergence Loss](https://mlsanity.com/paper/2011.07189/) (2020)
- [MFGNet: Dynamic Modality-Aware Filter Generation for RGB-T Tracking](https://mlsanity.com/paper/2107.10433/) (2021)
- [Exploring Fusion Strategies for Accurate RGBT Visual Object Tracking](https://mlsanity.com/paper/2201.08673/) (2022)
- [TextFusion: Unveiling the Power of Textual Semantics for Controllable Image Fusion](https://mlsanity.com/paper/2312.14209/) (2023)
- [An Accelerated Correlation Filter Tracker](https://mlsanity.com/paper/1912.02854/) (2019)
- [Temporal Aggregation for Adaptive RGBT Tracking](https://mlsanity.com/paper/2201.08949/) (2022)

## Abstract (arXiv)

RGBT tracking draws increasing attention because its robustness in multi-modal warranting (MMW) scenarios, such as nighttime and adverse weather conditions, where relying on a single sensing modality fails to ensure stable tracking results. However, existing benchmarks predominantly contain videos collected in common scenarios where both RGB and thermal infrared (TIR) information are of sufficient quality. This weakens the representativeness of existing benchmarks in severe imaging conditions, leading to tracking failures in MMW scenarios. To bridge this gap, we present a new benchmark considering the modality validity, MV-RGBT, captured specifically from MMW scenarios where either RGB (extreme illumination) or TIR (thermal truncation) modality is invalid. Hence, it is further divided into two subsets according to the valid modality, offering a new compositional perspective for evaluation and providing valuable insights for future designs. Moreover, MV-RGBT is the most diverse benchmark of its kind, featuring 36 different object categories captured across 19 distinct scenes. Furthermore, considering severe imaging conditions in MMW scenarios, a new problem is posed in RGBT tracking, named `when to fuse', to stimulate the development of fusion strategies for such scenarios. To facilitate its discussion, we propose a new solution with a mixture of experts, named MoETrack, where each expert generates independent tracking results along with a confidence score. Extensive results demonstrate the significant potential of MV-RGBT in advancing RGBT tracking and elicit the conclusion that fusion is not always beneficial, especially in MMW scenarios. Besides, MoETrack achieves state-of-the-art results on several benchmarks, including MV-RGBT, GTOT, and LasHeR. Github: https://github.com/Zhangyong-Tang/MVRGBT.