Lightweight RGB-T Tracking with Mobile Vision Transformers

• URL: http://arxiv.org/abs/2506.19154v1
• Date: Mon, 23 Jun 2025 21:46:22 GMT
• Title: Lightweight RGB-T Tracking with Mobile Vision Transformers
• Authors: Mahdi Falaki, Maria A. Amer,
• Abstract summary: We propose a novel lightweight RGB-T tracking algorithm based on Mobile Vision Transformers (MobileViT)<n>Compared to state-of-the-art efficient multimodal trackers, our model achieves comparable accuracy while offering significantly lower parameter counts.<n>This paper is the first to propose a tracker using Mobile Vision Transformers for RGB-T tracking and multimodal tracking at large.
• Score: 2.209921757303168
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Single-modality object tracking (e.g., RGB-only) encounters difficulties in challenging imaging conditions, such as low illumination and adverse weather conditions. To solve this, multimodal tracking (e.g., RGB-T models) aims to leverage complementary data such as thermal infrared features. While recent Vision Transformer-based multimodal trackers achieve strong performance, they are often computationally expensive due to large model sizes. In this work, we propose a novel lightweight RGB-T tracking algorithm based on Mobile Vision Transformers (MobileViT). Our tracker introduces a progressive fusion framework that jointly learns intra-modal and inter-modal interactions between the template and search regions using separable attention. This design produces effective feature representations that support more accurate target localization while achieving a small model size and fast inference speed. Compared to state-of-the-art efficient multimodal trackers, our model achieves comparable accuracy while offering significantly lower parameter counts (less than 4 million) and the fastest GPU inference speed of 122 frames per second. This paper is the first to propose a tracker using Mobile Vision Transformers for RGB-T tracking and multimodal tracking at large. Tracker code and model weights will be made publicly available upon acceptance.

Related papers

• Mamba-FETrack V2: Revisiting State Space Model for Frame-Event based Visual Object Tracking [9.353589376846902]
  We propose an efficient RGB-Event object tracking framework based on the linear-complexity Vision Mamba network.<n>The source code and pre-trained models will be released at https://github.com/Event-AHU/Mamba_FETrack.
  arXiv  Detail & Related papers  (2025-06-30T12:24:01Z)
• Heterogeneous Graph Transformer for Multiple Tiny Object Tracking in RGB-T Videos [31.910202172609313]
  Existing multi-object tracking algorithms generally focus on single-modality scenes.<n>We propose a novel framework called HGT-Track (Heterogeneous Graph Transformer based Multi-Tiny-Object Tracking)<n>This paper introduces the first benchmark VT-Tiny-MOT (Visible-Thermal Tiny Multi-Object Tracking) for RGB-T fused multiple tiny object tracking.
  arXiv  Detail & Related papers  (2024-12-14T15:17:49Z)
• SDSTrack: Self-Distillation Symmetric Adapter Learning for Multi-Modal Visual Object Tracking [19.50096632818305]
  Multimodal Visual Object Tracking (VOT) has recently gained significant attention due to its robustness. Recent studies have utilized prompt tuning to transfer pre-trained RGB-based trackers to multimodal data. We propose a novel symmetric multimodal tracking framework called SDSTrack.
  arXiv  Detail & Related papers  (2024-03-24T04:15:50Z)
• Tracking Meets LoRA: Faster Training, Larger Model, Stronger Performance [87.19164603145056]
  We propose LoRAT, a method that unveils the power of large ViT model for tracking within laboratory-level resources. The essence of our work lies in adapting LoRA, a technique that fine-tunes a small subset of model parameters without adding inference latency. We design an anchor-free head solely based on to adapt PETR, enabling better performance with less computational overhead.
  arXiv  Detail & Related papers  (2024-03-08T11:41:48Z)
• Bi-directional Adapter for Multi-modal Tracking [67.01179868400229]
  We propose a novel multi-modal visual prompt tracking model based on a universal bi-directional adapter. We develop a simple but effective light feature adapter to transfer modality-specific information from one modality to another. Our model achieves superior tracking performance in comparison with both the full fine-tuning methods and the prompt learning-based methods.
  arXiv  Detail & Related papers  (2023-12-17T05:27:31Z)
• Visual Prompt Multi-Modal Tracking [71.53972967568251]
  Visual Prompt multi-modal Tracking (ViPT) learns the modal-relevant prompts to adapt the frozen pre-trained foundation model to various downstream multimodal tracking tasks. ViPT outperforms the full fine-tuning paradigm on multiple downstream tracking tasks including RGB+Depth, RGB+Thermal, and RGB+Event tracking.
  arXiv  Detail & Related papers  (2023-03-20T01:51:07Z)
• Prompting for Multi-Modal Tracking [70.0522146292258]
  We propose a novel multi-modal prompt tracker (ProTrack) for multi-modal tracking. ProTrack can transfer the multi-modal inputs to a single modality by the prompt paradigm. Our ProTrack can achieve high-performance multi-modal tracking by only altering the inputs, even without any extra training on multi-modal data.
  arXiv  Detail & Related papers  (2022-07-29T09:35:02Z)
• Visible-Thermal UAV Tracking: A Large-Scale Benchmark and New Baseline [80.13652104204691]
  In this paper, we construct a large-scale benchmark with high diversity for visible-thermal UAV tracking (VTUAV) We provide a coarse-to-fine attribute annotation, where frame-level attributes are provided to exploit the potential of challenge-specific trackers. In addition, we design a new RGB-T baseline, named Hierarchical Multi-modal Fusion Tracker (HMFT), which fuses RGB-T data in various levels.
  arXiv  Detail & Related papers  (2022-04-08T15:22:33Z)
• Cross-Modal Object Tracking: Modality-Aware Representations and A Unified Benchmark [8.932487291107812]
  In many visual systems, visual tracking often bases on RGB image sequences, in which some targets are invalid in low-light conditions. We propose a new algorithm, which learns the modality-aware target representation to mitigate the appearance gap between RGB and NIR modalities in the tracking process. We will release the dataset for free academic usage, dataset download link and code will be released soon.
  arXiv  Detail & Related papers  (2021-11-08T03:58:55Z)
• Jointly Modeling Motion and Appearance Cues for Robust RGB-T Tracking [85.333260415532]
  We develop a novel late fusion method to infer the fusion weight maps of both RGB and thermal (T) modalities. When the appearance cue is unreliable, we take motion cues into account to make the tracker robust. Numerous results on three recent RGB-T tracking datasets show that the proposed tracker performs significantly better than other state-of-the-art algorithms.
  arXiv  Detail & Related papers  (2020-07-04T08:11:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.