Computer Vision-Based Visitor Behavior Analysis and Exhibition Layout Optimization in Museums

DOI

10.2991/978-94-6239-737-8_15How to use a DOI?

Keywords

Computer Vision; Visitor Behavior Analysis; Exhibition Optimization; Multi-Object Tracking; Museum Design; Data-Driven Methods

Abstract

Museum exhibition design increasingly relies on data-driven approaches to enhance visitor experience and spatial efficiency. This paper proposes a computer vision-based framework for visitor behavior analysis and exhibition layout optimization. The framework integrates object detection and multi-object tracking to capture visitor trajectories, dwell time, and spatial distribution from video data. Based on these features, a quantitative analysis model is constructed to evaluate exhibition performance, and a data-driven optimization strategy is developed to improve layout efficiency and reduce congestion. Comparative experiments are conducted using multiple baseline methods, and the results demonstrate that the proposed method achieves higher detection accuracy and more reliable behavior analysis. In addition, the optimized layout significantly improves spatial utilization and visitor flow. The proposed framework provides an effective and scalable solution for intelligent museum exhibition design.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Xinyu Guo
AU  - Yang Liu
AU  - Wenchao Luo
AU  - Qi Chen
PY  - 2026
DA  - 2026/08/18
TI  - Computer Vision-Based Visitor Behavior Analysis and Exhibition Layout Optimization in Museums
BT  - Proceedings of the 2026 5th International Conference on Art Design and Digital Technology (ADDT 2026)
PB  - Atlantis Press
SP  - 111
EP  - 117
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6239-737-8_15
DO  - 10.2991/978-94-6239-737-8_15
ID  - Guo2026
ER  -