Visitor modeling using computer vision and machine learning for the provision of augmented reality educational content and games
MuseLearn proposes to develop a system which will provide access to additional personalized digital material and will exploit technologies for object detection and visitor tracking. The intended outcome is the increase of the museum attendance.
The specific goals are:
- To develop a service for the visual detection of museum exhibits so that personalized supplementary digital material can be presented such as multimedia, augmented reality
- To develop a service that will allow the objective and constant monitoring of the visitors’ behavior and the impact of the exhibition, thus giving a constant feedback to curators
- A mobile guide application that will be able to penetrate the global market by offering high customer satisfaction and low cost
- A guide application which will increase visitor satisfaction and museum attendance in the long term
- Dissemination of the results to spread the MuseLearn merit and to allow the obsolete presentation methods to evolve. Target groups are museum administration and curators, visitors, researchers, companies active in the culture domain.
We will focus on the financial viability of the results by:
- requiring minimum museum equipment apart from the server and
- the wifi allowing the visitors use their own devices (tablets or smart phones)