Analysis and prediction of real museum visitors' interests and preferences based on their behaviors

Humans behaviors and experiences in social spaces are believed to be the result of the processes of the mind that are influenced by the different features of these spaces. By observing humans behaviors and experiences, it can be feasible to read their level of interests, preferences in any social environments. However, making manual large scale observation of human behaviors using paper-and-pencil based method is a very difficult and complicated task. In this study, an attractive solution to this complicated task is discussed. Here, we used our network enabled multiple LIDARs pole based human tracking system in supporting to our solution. This system can robustly track humans in any social environments. Our solution provides an easy way to observe humans behaviors from human tracking data to read their interests, preferences inside any social spaces. Finally, we tested our solution using a large set of human tracking data from an art gallery of a real museum to validate its effectiveness.