Visitor-artwork Network Analysis using Object Detection with Image-retrieval Technique
SCIE 2021 / Advanced Engineering Informatics (Computer Science & Artificial Intelligence [Q1], IF=7.862)
Sukjoo Hong, Taeha Yi, Joosun Yum, and Ji-Hyun Lee
https://doi.org/10.1016/j.aei.2021.101307
Highlights
1. Tracking museum visitors using object detection techniques (Mask R-CNN)
2. Analyze the relationship between visitors and artwork via a network analysis (bipartiate graph)
3. Propose a novel method of tracking visitors by using deep neural network technique
Abstract
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 visitor-centered 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 visitor-centered approach.
Fig. Framework of the visitor-centered artwork network analysis