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Diff-Tracker: Text-to-Image Diffusion Models are Unsupervised Trackers

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BERJAYA Computer Vision – ECCV 2024 (ECCV 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 15086))

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Abstract

We introduce Diff-Tracker, a novel approach for the challenging unsupervised visual tracking task leveraging the pre-trained text-to-image diffusion model. Our main idea is to leverage the rich knowledge encapsulated within the pre-trained diffusion model, such as the understanding of image semantics and structural information, to address unsupervised visual tracking. To this end, we design an initial prompt learner to enable the diffusion model to recognize the tracking target by learning a prompt representing the target. Furthermore, to facilitate dynamic adaptation of the prompt to the target’s movements, we propose an online prompt updater. Extensive experiments on five benchmark datasets demonstrate the effectiveness of our proposed method, which also achieves state-of-the-art performance.

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Acknowledgements

This research is supported by the Ministry of Education, Singapore, under the AcRF Tier 2 Projects (MOE-T2EP20222-0009 and MOE-T2EP20123-0014), and the National Research Foundation Singapore through its AI Singapore Programme (AISG-100E-2023-121).

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Authors and Affiliations

  1. Singapore University of Technology and Design, Singapore, Singapore

    Zhengbo Zhang, Li Xu, Duo Peng & Jun Liu

  2. Lancaster University, Lancaster, UK

    Hossein Rahmani & Jun Liu

Authors
  1. Zhengbo Zhang
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  2. Li Xu
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  3. Duo Peng
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  4. Hossein Rahmani
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  5. Jun Liu
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Corresponding author

Correspondence to Jun Liu .

Editor information

Editors and Affiliations

  1. University of Birmingham, Birmingham, UK

    Aleš Leonardis

  2. University of Trento, Trento, Italy

    Elisa Ricci

  3. Technical University of Darmstadt, Darmstadt, Germany

    Stefan Roth

  4. Princeton University, Princeton, NJ, USA

    Olga Russakovsky

  5. Czech Technical University in Prague, Prague, Czech Republic

    Torsten Sattler

  6. École des Ponts ParisTech, Marne-la-Vallée, France

    Gül Varol

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Zhang, Z., Xu, L., Peng, D., Rahmani, H., Liu, J. (2025). Diff-Tracker: Text-to-Image Diffusion Models are Unsupervised Trackers. In: Leonardis, A., Ricci, E., Roth, S., Russakovsky, O., Sattler, T., Varol, G. (eds) Computer Vision – ECCV 2024. ECCV 2024. Lecture Notes in Computer Science, vol 15086. Springer, Cham. https://doi.org/10.1007/978-3-031-73390-1_19

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