Detecting change in graffiti using a hybrid framework

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.