Geert J. Verhoeven

PhD Archaeology


Curriculum vitae



University of Vienna

Franz-Klein-Gasse 1
Room A5.04 (5th floor)
1190 Vienna
Austria



Detecting change in graffiti using a hybrid framework


Journal article


Benjamin Wild, Geert Julien Joanna Verhoeven, Rafał Muszyński, Norbert Pfeifer
The Photogrammetric Record, Early view, 2024


Cite

Cite

APA   Click to copy
Wild, B., Verhoeven, G. J. J., Muszyński, R., & Pfeifer, N. (2024). Detecting change in graffiti using a hybrid framework. The Photogrammetric Record, Early view. https://doi.org/10.1111/phor.12496


Chicago/Turabian   Click to copy
Wild, Benjamin, Geert Julien Joanna Verhoeven, Rafał Muszyński, and Norbert Pfeifer. “Detecting Change in Graffiti Using a Hybrid Framework.” The Photogrammetric Record Early view (2024).


MLA   Click to copy
Wild, Benjamin, et al. “Detecting Change in Graffiti Using a Hybrid Framework.” The Photogrammetric Record, vol. Early view, 2024, doi:10.1111/phor.12496.


BibTeX   Click to copy

@article{wild2024a,
  title = {Detecting change in graffiti using a hybrid framework},
  year = {2024},
  journal = {The Photogrammetric Record},
  volume = {Early view},
  doi = {10.1111/phor.12496},
  author = {Wild, Benjamin and Verhoeven, Geert Julien Joanna and Muszyński, Rafał and Pfeifer, Norbert}
}

Abstract
Graffiti, by their very nature, are ephemeral, sometimes even vanishing before creators finish them. This transience is part of graffiti's allure yet signifies the continuous loss of this often disputed form of cultural heritage. To counteract this, graffiti documentation efforts have steadily increased over the past decade. One of the primary challenges in any documentation endeavour is identifying and recording new creations. Image-based change detection can greatly help in this process, effectuating more comprehensive documentation, less biased digital safeguarding and improved understanding of graffiti. This paper introduces a novel and largely automated image-based graffiti change detection method. The methodology uses an incremental structure-from-motion approach and synthetic cameras to generate co-registered graffiti images from different areas. These synthetic images are fed into a hybrid change detection pipeline combining a new pixel-based change detection method with a feature-based one. The approach was tested on a large and publicly available reference dataset captured along the Donaukanal (Eng. Danube Canal), one of Vienna's graffiti hotspots. With a precision of 87% and a recall of 77%, the results reveal that the proposed change detection workflow can indicate newly added graffiti in a monitored graffiti-scape, thus supporting a more comprehensive graffiti documentation.




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