Lightweight RGB-T Tracking with Mobile Vision Transformers
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915769166594048 |
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| author | Falaki, Mahdi Amer, Maria A. |
| author_facet | Falaki, Mahdi Amer, Maria A. |
| contents | Single-modality tracking (RGB-only) struggles under low illumination, weather, and occlusion. Multimodal tracking addresses this by combining complementary cues. While Vision Transformer-based trackers achieve strong accuracy, they are often too large for real-time. We propose a lightweight RGB-T tracker built on MobileViT with a progressive fusion framework that models intra- and inter-modal interactions using separable mixed attention. This design delivers compact, effective features for accurate localization, with under 4M parameters and real-time performance of 25.7 FPS on the CPU and 122 FPS on the GPU, supporting embedded and mobile platforms. To the best of our knowledge, this is the first MobileViT-based multimodal tracker. Model code and weights are available in the GitHub repository. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_19154 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Lightweight RGB-T Tracking with Mobile Vision Transformers Falaki, Mahdi Amer, Maria A. Computer Vision and Pattern Recognition Single-modality tracking (RGB-only) struggles under low illumination, weather, and occlusion. Multimodal tracking addresses this by combining complementary cues. While Vision Transformer-based trackers achieve strong accuracy, they are often too large for real-time. We propose a lightweight RGB-T tracker built on MobileViT with a progressive fusion framework that models intra- and inter-modal interactions using separable mixed attention. This design delivers compact, effective features for accurate localization, with under 4M parameters and real-time performance of 25.7 FPS on the CPU and 122 FPS on the GPU, supporting embedded and mobile platforms. To the best of our knowledge, this is the first MobileViT-based multimodal tracker. Model code and weights are available in the GitHub repository. |
| title | Lightweight RGB-T Tracking with Mobile Vision Transformers |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2506.19154 |