Browse State-of-the-Art › Rgb-T Tracking
Rgb-T Tracking
25 papers with code · 4 benchmarks · 2 datasets archive 2025-07-28
RGBT tracking, or RGB-Thermal tracking, is a sophisticated method utilized in computer vision for tracking objects across both RGB and thermal infrared modalities. This technique combines information from both RGB and thermal imagery to enhance object detection and tracking performance, particularly in challenging environments where lighting conditions may vary or be limited. By integrating data from these two modalities, RGBT tracking systems can effectively compensate for the limitations of each individual modality, such as the inability of RGB cameras to capture clear images in low-light or adverse weather conditions, and the inability of thermal cameras to accurately identify object details. RGBT tracking algorithms typically involve sophisticated fusion techniques to combine information from RGB and thermal sensors, enabling robust and accurate object tracking in diverse scenarios ranging from surveillance and security applications to autonomous vehicles and search and rescue operations.
Description from the archive archive 2025-07-28.
Benchmarks archive 2025-07-28
4 leaderboard tables shown for this task, 4 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted.
| Dataset | Best model (first row in archive order) | Paper | Code | Syntology | Compare |
|---|---|---|---|---|---|
| RGBT234 (42 rows) | SUTrack-L224 | SUTrack: Towards Simple and Unified Single Object Tracking | code | — | Compare |
| LasHeR (39 rows) | FlexTrack | — | — | — | Compare |
| RGBT210 (19 rows) | PromptTrack | — | — | — | Compare |
| GTOT (15 rows) | MambaVT-M256 | MambaVT: Spatio-Temporal Contextual Modeling for robust RGB-T Tracking | code | — | Compare |
Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.
Libraries
Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.
Datasets archive 2025-07-28
2 datasets whose archive record lists this task, ordered by the archive's paper count.
Subtasks archive 2025-07-28
No subtask under this task in the archive's task tree.
Parent tasks archive 2025-07-28
Most implemented papers archive 2025-07-28
25 shown of 25 papers with code (55 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.
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9 Mar 2024 4 repositories listedCurrent event-/frame-event based trackers undergo evaluation on short-term tracking datasets, however, the tracking of real-world scenarios involves long-term tracking, and the performance of existing tracking…
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26 Jan 2022 2 repositories listedRGBT tracking usually suffers from various challenging factors of fast motion, scale variation, illumination variation, thermal crossover and occlusion, to name a few.
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22 Jul 2021 2 repositories listedThe visible and thermal filters will be used to conduct a dynamic convolutional operation on their corresponding input feature maps respectively.
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8 Dec 2020 2 repositories listedVisual object tracking, as a fundamental task in computer vision, has drawn much attention in recent years.
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6 May 2025 1 repository listed Syntology ran 6 of 8 samples · 2 unverified · 8 pointer-only (licence)GDSTrack dynamically fuses the modalities of neighboring frames, treats them as distractor noise, and leverages the denoising capability of a generative model.
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26 Dec 2024 1 repository listedIt consolidates five SOT tasks (RGB-based, RGB-Depth, RGB-Thermal, RGB-Event, RGB-Language Tracking) into a unified model trained in a single session.
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20 Dec 2024 1 repository listed Syntology ran 3 of 8 samples · 5 unverifiedThese temporal information tokens are used to guide the localization of the target in the next time state, establish long-range contextual relationships between video frames, and capture the temporal trajectory of the…
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15 Oct 2024 1 repository listedTo handle this issue, we take original RGB and TIR networks as the teachers, and distill their content knowledge into two student networks respectively by the style-content orthogonal feature decoupling scheme.
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15 Aug 2024 1 repository listedExisting RGB-T tracking algorithms have made remarkable progress by leveraging the global interaction capability and extensive pre-trained models of the Transformer architecture.
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6 May 2024 1 repository listedThe former does not fully exploit the potential of using only RGB and TIR information of the template or search region for channel and spatial feature fusion, and the latter lacks direct interaction between the template…
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4 May 2024 1 repository listedIn particular, we design a fusion structure space based on the hierarchical attention network, each attention-based fusion unit corresponding to a fusion operation and a combination of these attention units…
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30 Apr 2024 1 repository listedWe propose a new method based on a mixture of experts, namely MoETrack, as a baseline fusion strategy.
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24 Mar 2024 1 repository listed Syntology ran 1 of 1 samples · 0 unverifiedMultimodal Visual Object Tracking (VOT) has recently gained significant attention due to its robustness.
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17 Dec 2023 1 repository listedTo handle this problem, we propose a novel multi-modal visual prompt tracking model based on a universal bi-directional adapter, cross-prompting multiple modalities mutually.
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27 Nov 2023 1 repository listedIn practice, most existing RGB trackers learn a single set of parameters to use them across datasets and applications.
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4 Sep 2023 1 repository listedIn this context, we seek to uncover the potential of harnessing generative techniques to address the critical challenge, information fusion, in multi-modal tracking.
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31 Aug 2023 1 repository listedObject tracking based on the fusion of visible and thermal im-ages, known as RGB-T tracking, has gained increasing atten-tion from researchers in recent years.
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26 Aug 2023 1 repository listedWith this structure, the network can extract fusion features of the template and search region under the mutual interaction of modalities.
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27 Apr 2023 1 repository listedIn this paper, we introduce a new sequence-to-sequence learning framework for RGB-based and multi-modal object tracking.
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20 Mar 2023 1 repository listed Syntology ran 1 of 1 samples · 0 unverifiedTo inherit the powerful representations of the foundation model, a natural modus operandi for multi-modal tracking is full fine-tuning on the RGB-based parameters.
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1 Jan 2023 1 repository listedTo alleviate these limitations, we propose a novel Template-Bridged Search region Interaction (TBSI) module which exploits templates as the medium to bridge the cross-modal interaction between RGB and TIR search regions…
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8 Apr 2022 1 repository listedWith the popularity of multi-modal sensors, visible-thermal (RGB-T) object tracking is to achieve robust performance and wider application scenarios with the guidance of objects' temperature information.
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27 Apr 2021 1 repository listedRGBT tracking receives a surge of interest in the computer vision community, but this research field lacks a large-scale and high-diversity benchmark dataset, which is essential for both the training of deep RGBT…
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30 Aug 2019 1 repository listedOur tracker is trained in an end-to-end manner, enabling the components to learn how to fuse the information from both modalities.
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1 Sep 2018 1 repository listedTo address this problem, this paper presents a novel approach to suppress background effects for RGB-T tracking.
Syntology lines on 4 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.
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