Visitor-artwork network analysis using object detection with image-retrieval technique

Recent museum exhibitions are becoming a means by which to satisfy visitor demands. In order to provide visitor-centric exhibitions, artwork must be analyzed based on the behavior of visitors, and not merely according to museum professionals' points of view. This study aims to analyze the relationship between museum visitors and artwork via a network analysis based on visitor behavior using object detection techniques. Cameras installed in a museum recorded visitors, and an object detector with a content-based image-retrieval technique tracked visitors from the videos. The durations spent with different artworks were measured, and the data was converted into a bipartite graph. The relationships between different artwork types were analyzed with a visitorcentered artwork network. Based on the visitors' behavior, significant artworks were identified and the artwork network was compared to the arrangement of the museum. The tendency of edges in the artwork network was also examined considering visitors' preferences for artworks. The method used here makes it possible to collect quantitative data, with the results possibly used as a basis and for reference when analyzing artwork in a visitorcentered approach.

Automated summary

Recent museum exhibitions are designed to meet visitor demands, with artwork analyzed based on visitor behavior to create a visitor-centric approach. The study uses object detection techniques and network analysis to understand the relationship between museum visitors and artwork, identifying significant artworks and comparing them to the museum layout. This method allows for quantitative data collection and can serve as a basis for analyzing artwork in a visitor-centered approach.