Abstract
The analysis of the success of any exhibition depends on the visitor experience. The data required for analysis is usually painstakingly collected by hand. We propose a large-scale optical tracking pipeline to estimate visitor data such as visit trajectory, duration, and, potentially, other personal parameters like age, weight, and sex, yet remain ethically acceptable by obtaining visitor consent. We further show, in preliminary results, that the edge device has a localization error of 0.64 meters and an average precision of 0.2. With this work-in-progress, we intend to ensure a viable alternative to current data collection processes in museum research.Abstract
Reference12 articles.
1. Bradski, Gary (2000). The OpenCV Library. Dr. Dobb’s Journal of Software Tools, 122–25. Available online at https://www.proquest.com/trade-journals/opencv-library/docview/202684726/se-2 (all URLs here accessed in August 2023).
2. De Angelis, Alessio/Francesco, Santoni (2022). Advanced Sensors and Sensing Technologies for Indoor Localization. Applied Sciences 12 (8), 3786. https://doi.org/10.3390/app12083786.
3. Eade, Ethan (2013). Gauss‐Newton / Levenberg‐Marquardt Optimization. https://ethaneade.com/optimization.pdf.
4. Kuflik, Tsvi/Lanir, Joel/Dim, Eyal et al. (2011). Indoor Positioning: Challenges and Solutions for Indoor Cultural Heritage Sites. International Conference on Intelligent User Interfaces, Proceedings IUI, 375–78. https://doi.org/10.1145/1943403.1943469.
5. Meints, Martin/Biermann, Heinz/Bromba, Manfred et al. (2008). Biometric Systems and Data Protection Legislation in Germany. 2008 International Conference on Intelligent Information Hiding and Multimedia Signal Processing, 1088–93. https://doi.org/10.1109/iih-msp.2008.314.